Method and system for insect sorting and imaging

Abstract

An insect sorting module includes a receiving area for receiving a tray having a plurality of insect samples thereon. The camera is configured to capture an image of the plurality of insect samples on the tray when the tray is received within the receiving region. The computing device communicates with the camera. The camera and the computing device cooperate to define an image processing system configured to identify a location of an individual insect sample of the plurality of insect samples on the tray. The module comprises an experiment board area used for receiving an experiment board. The robot is configured to pick up the individual insect sample based on the individual insect sample location identified by the image processing system and place the individual insect sample into the experimental board of the experimental board area.

Description

[0001] Cross-references to related applications This application claims priority to and benefit from the filing date of U.S. Provisional Patent Application No. 63 / 517,497, filed August 3, 2023, the entire contents of which are incorporated herein by reference.

[0002] field The present embodiments of the present invention generally relate to methods and systems for sorting and imaging insects (including egg and larval stages), which can be used for automated high-throughput bioassays.

[0003] background There has long been a need for agriculturally important compositions and methods for controlling or eradicating insect pests. Simultaneously, there is an urgent need for agriculturally important high-throughput methods and systems for screening candidate compositions, as well as methods and systems for controlling or eradicating insect pests.

[0004] Brief Systems and methods for insect sorting are provided. In some embodiments, the module for insect sorting includes: a receiving area for receiving a tray having multiple insect samples thereon; a camera configured to capture images of the multiple insect samples on the tray when the tray is received within the receiving area; a computing device communicating with the camera; the camera and computing device cooperating to define an image processing system configured to identify the position of an individual insect sample among the multiple insect samples on the tray; the module including an experimental board area for receiving experimental boards; and a robot configured to pick up individual insect samples based on the positions of the individual insect samples identified by the image processing system and place the individual insect samples into experimental boards within the experimental board area.

[0005] In some implementations, the module includes an experimental plate area for receiving experimental plates; and an insect sample dispenser configured to dispense insect samples into experimental plates within the experimental plate area; the insect sample dispenser includes a receiving space configured to receive multiple insect samples; a metering tray has multiple holes configured to receive corresponding insect samples from the multiple insect samples from the receiving space; the multiple holes are arranged to be positioned above corresponding cavities of the experimental plates.

[0006] The sliding gate can slide relative to the metering tray to release insect samples from multiple wells into the experimental plate within the experimental plate area.

[0007] In some implementations, the module includes a CO2 supply configured to sedate multiple insects; and a feeder configured to dispense the sedated insects.

[0008] The system includes a module for sorting insect samples and a service robot configured to transport pallets to and from the module.

[0009] Methods for using the module and system are also disclosed. Attached Figure Description

[0010] Figure 1 A perspective view showing a non-limiting example of an automated insect bioassay system.

[0011] Figure 2 Show Figure 1 A top view of the automated insect bioassay system.

[0012] Figure 3 Show Figure 1 A top view of the sorting module of the automated insect bioassay system.

[0013] Figure 4 Show Figure 1 Side view of the sorting module of the automated insect bioassay system.

[0014] Figure 5 Show Figure 4 The sorting module of the automated insect bioassay system is based on Figure 4 A sectional view taken from the AA plane.

[0015] Figure 6 This illustration shows a top-view, non-limiting example of an automated insect bioassay system. The example depicts a sorting module, a puncture assembly, a sealing assembly, an evaporator, a first incubator, an imaging assembly, a plate storage assembly, a plate stacking assembly, and a robotic arm.

[0016] Figure 7 Show Figure 6 A non-limiting example of a side-view view of an automated insect bioassay system. This example illustrates a sorting module, a puncture assembly, a sealing assembly, an evaporator, a first incubator, a second incubator, an imaging assembly, a plate storage assembly, and a plate stacking assembly.

[0017] Figure 8A A second, non-limiting example of an automated insect bioassay system is shown from a top-view perspective. This example illustrates a first sorting module, a puncture assembly, a sealing assembly, an evaporator, a first incubator, a second incubator, a first imaging assembly, a first plate storage assembly, a main computer, a second general-purpose computer, a third general-purpose computer, a second imaging assembly, a first robotic arm, a second robotic arm, a second plate storage assembly, a first barcode reader, a second barcode reader, a second sorting module, and a third sorting module.

[0018] Figure 8BA second, non-limiting example of an automated insect bioassay system is shown from a side view. This example illustrates a sorting module, a puncture assembly, a sealing assembly, an evaporator, a first incubator, a second incubator, a first imaging assembly, a main computer, a second general-purpose computer, and first and second plate stacking assemblies.

[0019] Figure 9 This is a schematic diagram depicting an exemplary arrangement of a computer system used with the automated insect bioassay system disclosed herein.

[0020] Figure 10 This is a cross-sectional perspective view of the components used to provide an air curtain.

[0021] Figure 11 This is an exploded view of the cooling system.

[0022] Figure 12 yes Figure 11 Exploded view of the tray of the central cooling system.

[0023] Figure 13 This is a perspective view of the cooling system portion disclosed in this article.

[0024] Figure 14 This is a perspective view of multiple end effector sections of the robot for the sorting module disclosed herein, showing different screen / filter types.

[0025] Figure 15 This is a perspective view of an exemplary end effector of the robot for the sorting module disclosed herein.

[0026] Figure 16 This is a perspective view of several exemplary end effectors of the robot for the sorting module disclosed herein.

[0027] Figure 17 Show Figure 16 Cross-sectional views of several exemplary end effectors.

[0028] Figure 18 A cross-sectional view of an exemplary end effector of a robot for the sorting module disclosed herein is shown.

[0029] Figure 19 Show Figure 18 A perspective view of an exemplary end effector.

[0030] Figure 20 A perspective view showing an exemplary end effector of the robot for the sorting module disclosed herein.

[0031] Figure 21The diagram shows a perspective view of an exemplary end effector assembly of a robot for a sorting module disclosed herein, the exemplary end effector assembly comprising a plurality of independently articulated end effectors.

[0032] Figure 22 Show Figure 21 A side view of an exemplary end effector component.

[0033] Figure 23 Show Figure 21 A front view of an exemplary end effector component.

[0034] Figure 24 An exemplary end effector component of a robot for a sorting module disclosed herein is shown, the exemplary end effector component having a single end effector.

[0035] Figure 25 An exemplary end effector component of a robot for the sorting module disclosed herein is shown.

[0036] Figure 26 shows a perspective view of several exemplary puncture elements.

[0037] Figure 27 An exploded view of an exemplary puncture element is shown.

[0038] Figure 28 A cross-sectional view of an exemplary sorting component disclosed herein is shown.

[0039] Figure 29A A side view of the ladder feeder in the first configuration is shown. Figure 29B Show Figure 29A A side view of the ladder-type feeder in the second position.

[0040] Figure 30 Show Figures 29A-29B Top view of the ladder-type feeder.

[0041] Figure 31 A top view of an exemplary vibratory feeder is shown.

[0042] Figure 32 Show Figure 31 Side view of a vibrating feeder.

[0043] Figure 33 A block diagram of an exemplary system configured to use machine learning, as disclosed herein, is shown.

[0044] Detailed Explanation The embodiments of the present invention are not limited to the exemplary methods and materials disclosed, and any similar or equivalent methods and materials may be used in the practice or testing of the embodiments of the present invention. Numerical ranges include numbers that define the range.

[0045] Unless the context otherwise specifies, the articles “a” and “one” are used to refer to one or more (i.e., at least one) grammatical object of the article. For example, “one element” can refer to one or more elements.

[0046] As used in this article, "high hatching rate" means an insect egg hatching rate of at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 100%.

[0047] As used in this article, “insect” means all life stages of an insect or any single life stage of an insect, including but not limited to eggs and larvae.

[0048] As used herein, “IC-50” or inhibitory concentration and “EC-50” or effective concentration are used interchangeably and refer to the concentration at which the larval size (determined by the larval pixel area) is between the maximum size (zero-dose control) and the minimum size (highest toxic dose). (See Ritz (2010) Environmental Toxicology and Chemistry 29:220-229, Ali and Luttrell (2009) Journal of Economic Entomology 102:1935-1947, Brvault et al. (2009), Journal of Economic Entomology 102:2301-2309, Kerr and Meador (1996), Environmental Toxicology and Chemistry 15:395-401, Marcon et al. (1999) Journal of Economic Entomology 92:279-229).

[0049] As used herein, removing insect clumps includes, but is not limited to, removing, dissolving, breaking down, manually or mechanically separating clumps, clusters, or groups of insects. Insect clumps may include insect eggs and / or insect larvae. In one embodiment, removing insect clumps includes using a sieve. In another embodiment, removing insect clumps includes using enzymatic or chemical methods to break down the proteins that hold the insects together, such as digesting peptides that hold eggs together.

[0050] In one embodiment of the invention, a method for sorting insects with a high hatching rate is provided, comprising rinsing the insects with a rinsing solution; discarding floating insects from the rinsing solution; disinfecting the insects; separating immature insects from mature insects; and sorting the mature insects using a sorting module. In another embodiment, a method for sorting insects with a high hatching rate is provided, comprising removing insect clumps after the insect eggs have hatched; hatching the insects until they show signs of development; and sorting the mature insects using a sorting module. In yet another embodiment, insects that are unlikely to survive or whose eggs are unlikely to hatch are discarded.

[0051] In some implementations, these methods are applicable to insects selected from Coleoptera, Diptera, Hymenoptera, Lepidoptera, Trichophala, Homoptera, Hemiptera, Orthoptera, Thysanoptera, Dermoptera, Isoptera, Psoriata, Siphonaptera, and Trichoptera, especially Lepidoptera and Coleoptera.

[0052] Lepidoptera larvae include, but are not limited to: armyworms, root-cutting moths, bridge-building moths, and fall armyworms (family Noctuidae). Spodoptera frugiperda JE Smith), beet armyworm ( S. exigua Hübner), Spodoptera litura ( S. litura Fabricius, also known as the tobacco root-cutting worm or the tufted caterpillar), and the bud-banded noctuid moth ( Mamestra configurata Walker), cabbage looper ( M. brassicae Linnaeus, small root-cutting insect ( Agrotis ipsilon Hufnagel), Western root-cutting worm ( A. orthogonia Morrison, Gracilaria granulosa ( A. subterranea Fabricius), cotton leaf ripple moth ( Alabama argillacea Hübner), Powdered Noctuid ( Trichoplusia ni Hübner), soybean looper moth ( Pseudoplusia includens Walker, Lidou Noctuid ( Anticarsia gemmatalis Hübner, Green Alfalfa Moth ( Hypena scabra Fabricius, tobacco bud moth ( Heliothis virescens Fabricius, One-Star Sludge Belly ( Pseudaletia unipuncta Haworth, rough-skinned root-cutting worm ( Athetis mindara Barnes and Mcdunnough, Dark Side-cutting Root Worm ( Euxoa messoria Harris), Emerald Diamond ( Earias insulana Boisduval, Dingdian Diamond ( E. vittella Fabricius, American cotton bollworm ( Helicoverpa armigera Hübner, corn ear worm or cotton bollworm ( H. zea Boddie, spotted noctuid moth ( Melanchra picta Harris), Citrus root-cutting worm ( Egira (Xylomyges) curialis Grote; Pyralidae family: stem borers, sheath moths, leaf-bearing moths, fruit borers, and web-forming moths; European corn borer ( Ostrinia nubilalis Hübner), navel orange borer ( Amyelois transitella Walker), Mediterranean mealybug ( Anagasta kuehniella Zeller), Powdery moth ( Cadra cautella Walker), stem borer ( Chilo suppressalis Walker), sorghum stem borer ( C. partellus ), rice moth ( Corcyra cephalonica Stainton, corn root borer ( Crambus caliginosellus Clemens), early-maturing grass moth ( C. teterrellus Zincken, rice leaf roller ( Cnaphalocrocis medinalis Guenée, grape leafroller ( Desmia funeralis Hübner), melon borer ( Diaphania hyalinata Linnaeus, Cucumber Moth ( D. nitidalis Stoll), Southwest Corn Borer ( Diatraea grandiosella Dyar), sugarcane borer ( D. saccharalis Fabricius, Mexican rice borer ( Eoreuma loftini Dyar), tobacco moth ( Ephestia elutella Hübner, also known as the cocoa borer, and the large wax borer ( Galleria mellonella Linnaeus, grassland moth ( Herpetogramma licarsisalis Walker), sunflower borer ( Homoeosoma electellum Hulst, small corn stem borer ( Elasmopalpus lignosellus Zeller), small wax moth ( Achroia grisella Fabricius, beet borer ( Loxostege sticticalis Linnaeus, tea leaf borer ( Orthaga thyrisalis Walker), Bean Pod Moth ( Maruca testulalis Geyer), Indian meal moth ( Plodia interpunctella Hübner), rice stem borer ( Scirpophaga incertulas Walker), celery leaf roller ( Red-bellied woodpecker Guenée; and leafrollers, bud moths, seed moths, and fruit moths in the family Tortricidae: West Blackhead Bud Moth ( Acleris gloverana Walsingham), East Blackhead Moth ( A. various Fernald), Brown-banded roller ( Archips argyrospila Walker), European leafroller ( A. rosana Linnaeus and other species of the genus Archipelago, including the summer fruit roller ( Adoxophyes orana Fischer von Rösslerstamm), sunflower leaf roller ( The snail is a guest. Walsingham, hazelnut leafroller ( Cydia latifera Walsingham, codling moth ( C. pomonella Linnaeus, variegated leafroller ( Platinum yellow Clemens), omnivorous leafroller ( P. stultana Walsingham, European grape vine leafroller ( Lobesia botrana Denis & Schiffermüller, Eyed Bud Moth ( Spilonota ocellana Denis & Schiffermüller, Grape leafroller ( Endopiza viteana Clemens), grape bunch moth ( Eupoecilia ambiguous Hübner), Brazilian apple leafroller ( Bonagota salubricola Meyrick), Pear Fruit Worm ( Grapholite pest Busck), sunflower bud roller ( Suleima helianthana Riley), genus *Rieley*, genus *Rieley* ( Argyrotaenia spp. ); Leafroller ( ) Choristoneura spp. ).

[0053] Other selected agricultural pests in the order Lepidoptera include, but are not limited to: the fall webworm (…). Alsophila pometaria Harris); Peach branch moth ( Anarsia lineatella Zeller); Orange-banded Oak Butterfly ( Senatorial Anisot JE Smith); Oak silkworm ( Antheraea pernyi Guérin-Méneville); Bombyx mori ( The silkworm dies. Linnaeus); cotton leafminer ( Bucculatrix thurberiella Busck); Alfalfa White Butterfly ( Colias eurythema Boisduval); Walnut boat moth ( The most complete data Grote & Robinson; Siberian pine caterpillar ( Siberian Dendrolimus Tschetwerikov); Elm stepper moth ( Ennomos' signatures Hübner; Bodhi inchworm ( Erannis tiliaria Harris); Yellow tussock moth ( Euproctis chrysorrhoea Linnaeus; Grape leaf spot moth ( American Harrisia Guérin-Méneville); Pasture caterpillars ( Olive leaf Cockrell); American white moth ( Hyphantria wedge Drury); Tomato wheat moth ( Keiferia lycopersicella Walsingham; Eastern Hemlock Looper ( Lambdina fiscellaria boxwood Hulst); Western Hemlock Looper ( L. fiscellaria lugubrosa Hulst); Willow Moth ( Willow leucoma Linnaeus); Gypsy moth ( Lymantria dispar Linnaeus; Five-spotted moth ( Eat five-spotted Haworth, also known as the tomato hawk moth; tomato hawk moth ( M. sixth Haworth, also known as the tobacco hawk moth; winter geometrid moth ( Operophtera brumata Linnaeus); Spring Measuring Moth ( Paleacrita vernata Peck); Dalmata butterfly ( Butterfly cresphontes Cramer); California oak leafminer ( Phryganidia californica Packard); Citrus leafminer ( Phyllocnistis citrella Stainton); Leaf miner ( ) Phyllonorycter blancardella Fabricius); Giant cabbage white butterfly ( Cabbage Pieris Linnaeus; small cabbage white butterfly ( P. rapae Linnaeus); Green-veined cabbage white butterfly ( P. napi Linnaeus; Artichoke moth ( Platyptilia carduidactyl Riley); Diamondback moth ( Plutella xylostella Linnaeus); Bollworm ( Pectinophora gossypiella Saunders; Southern cabbage white butterfly ( Pontia protodice Boisduval and Leconte); Omnivorous inchworm ( Sick sandflies Guenée); Red-backed boat moth ( Schizura concinna JE Smith); Wheat moth ( Sitotroga cerealella Olivier); Pine-leaved moth ( Thaumetopoea pityocampa Schiffermuller); Bagworm ( Tineola bisselliella Hummel); Tomato leaf miner ( Absolutely safe Meyrick); Nesting moth ( Yponomeuta panLinnaeus; *Pseudo-tuberculosa* ( ) Heliothis subflexa Guenée); genus *Guenée* (leaf moth) Malacosoma spp. ) and the genus *Pyrethrum* ( Orgyia spp. ).

[0054] Interesting are the larvae and adults of the order Coleoptera, including weevils of the families Anthribidae, Bruchidae, and Curculionidae (including but not limited to: the cotton boll weevil). Anthonomus grandis Boheman); Rice water weevil ( Lissorhoptrus oryzophilus Kuschel); Valley Elephant ( Sitophilus granarius Linnaeus); Rice elephant ( S. oryzae Linnaeus; Clover Leaf Weevil ( Hyper dotted Fabricius); Sunflower stem weevil ( Cylindrocopterus adspersus LeConte); Sunflower Red Seed Weevil ( Yellow-tailed eagle LeConte); Sunflower Grey Seed Weevil ( S. sordidus LeConte); Corn weevil ( Sphenophorus maidis Chittenden); flea beetles, cucumber beetles, root beetles, leaf beetles, potato beetles, and leaf miners (including but not limited to: potato leaf beetles) of the Chrysomelidae family. Leptinotarsa ​​ten-lineata Say); Western corn rootworm ( Diabrotica virgifera virgifera LeConte); Northern corn rootworm ( D. barbers Smith and Lawrence; Southern corn rootworm ( D. undecimpunctata howardi Barber); Corn Flea Beetle ( Chaetocnema pulicaria Melsheimer); Cruciferae flea beetle ( Phyllotreta cruciferous Goeze); Yellow-striped flea beetle ( Phyllotreta striolata ); Grape brown leaf beetle ( Collapse brown Fabricius); Black-jawed mud beetle ( Oulema melanopus Linnaeus); Sunflower leaf beetle ( Zygogram of exclamation Fabricius); beetles of the family Coccinellidae (including but not limited to: Mexican bean ladybug ( Epilachna varivesti Mulsant); scarab beetles and other beetles of the Scarabaeidae family (including but not limited to: Japanese scarab beetle). Japanese popillia Newman); Northern Round-headed Rhinoceros Beetle ( Cyclocephalus borealis Arrow, grub; Southern round-headed rhinoceros beetle ( C. immaculata Olivier (a type of grub); European scarab beetle ( Rhizotrogus majalis Razoumowsky; Hairy Golden Beetle ( Phyllophaga hairy Burmeister (a grub); Carrot beetle ( Ligyrus gibbosus De Geer); the skipjack tuna of the family Dermestidae; wireworms and tenebrionids of the family Elateridae. Eleodes spp. ), Black-spotted beetle genus ( Melanotan spp. ); *Broad-chested clasper* ( ) Conoderus spp. ); concave forehead and thorax ( Limonius spp. ); Crops (genus *Crops*) Agriotes spp. ); genus *Ctenopharynx* ( Ctenicera spp. ); refers to the genus *Tegus* ( Aeolus spp. Bark beetles of the family Scolytidae and beetles of the family Tenebrionidae.

[0055] What is of interest are adults and immature individuals of the order Diptera, including leaf miners such as the corn leafminer (…). Agromyza small-corned Loew); gall midges (including but not limited to: sorghum gall midges ( Contarina sorghum Coquillett; Hessian gall midge ( Mayetiola destructor Say); Wheat wax stem sawfly ( Sitodiplosis mosellana Géhin; sunflower seed gall midge ( Neolasioptera murtfeldtiana Felt); fruit flies (Tephritidae), Swedish wheat straw flies ( Oscinella frit Linnaeus); fly larvae (including but not limited to: gray ground fly ( Delia's flat Meigen, also known as seed fly; wheat seed fly ( D. coarctata Fallen and other ground fly species (Delia spp.), American straw fly ( Meromyza americana Fitch); housefly ( Housefly Linnaeus); housefly ( Fannia canicularis Linnaeus, housefly ( F. femoral Stein); stable fly ( Stomoxys calcitrans Linnaeus); Flood flies, horned flies, blowflies, and golden flies ( ) Chrysomya spp. ); genus *Fly* ( Phormia spp. ) and other fly pests, horseflies ( Tabanus spp. ); genus *Gastropoda* ( Gastrophilus spp. ); genus *Cyclophorus* ( Oestrus spp. ); Fly genus ( Hypoderma spp. ); genus *Tetranychus* ( Chrysops spp. ); sheep tick ( Sheep's hawk Linnaeus and other suborders such as Brachycera, Aedes (… Aedes spp. Anopheles ( ) Anopheles spp. ); Culex genus ( Culex spp. ); Genus *Protoceras* ( Prosimulium spp. ); Genus *Frog* ( Simulium spp. (); Midges, sandflies, hawk-eyed fungus gnats, and other longhornoids.

[0056] Insects of interest include adults and nymphs of the orders Hemiptera and Homoptera, such as, but not limited to: the ball aphid of the family Adelgidae; the mirid bug of the family Miridae; the cicada of the family Cicadidae; and the leafhoppers and leafhoppers of the genus Cicadellidae. Empoasca spp. Planthoppers (Cixiidae), Flatidae, Fulgoroidea, Issidae, and Delphacidae); treehoppers (Membracidae); psyllids (Psyllidae); whiteflies (Aleyrodidae); aphids (Aphididae); phylloxera (Phylloxeridae); mealybugs (Pseudococcidae); starfish Scale insects belonging to the families Asterolecanidae, Coccidae, Dactylopiidae, Diaspididae, Eriococcidae, Ortheziidae, Phoenicococcidae, and Margarodidae; lace bugs of the family Tingidae; stink bugs of the family Pentatomidae; and long bugs and long bugs of the genus Lygaeidae. Blissus spp. Other seed bugs; grasshoppers of the family Cercopidae; melon bugs of the family Coreidae; and red bugs and cotton red bugs of the family Pyrrhocoridae.

[0057] Agronomically important members of the order Homoptera include, but are not limited to: the pea aphid ( Acyrthisiphon pea Harris); Alfalfa aphid ( Aphis spicatus Koch); broad bean aphid ( A. beans Scopoli); cotton aphid ( A. gossypii Glover (also known as melon aphid); corn root aphid ( A. maidaridicis Forbes); Apple aphid ( A. apple De Geer); Spiraea aphid ( A. spiraecola Patch); Eggplant Grove Aphid-Free ( Aulacorthum nightshade Kaltenbach; Strawberry trichotillo ( Chaetosiphon fragaefolii Cockerell); Russian wheat aphid ( Diuraphis noxious Kurdjumov / Mordvilko; Apple roundtail aphid ( Dysaphis plantain Paaserini; Apple woolly aphid ( Eriosoma woolly Hausmann; cabbage aphid ( Brassica oleracea Linnaeus; Peach-colored aphid ( Hyalopterus plumi Geoffroy); Radish aphid ( Lipaphis erysimi Kaltenbach); Wheat without web aphids ( Metopolophium dirrhodum Walker); Potato aphid ( Macrosiphum euphorbia Thomas); Peach-potato aphid ( Myzus persica Sulzer, also known as the peach aphid; lettuce aphid ( Nasonovia blackcurrant Mosley; Gall aphid ( ) Pemphigus spp., including root aphids and gall aphids; corn leaf aphids ( Rhopalosiphum maidis Fitch); Gramineae constrictor aphid ( R. paddy Linnaeus); wheat aphid ( Schizaphis grass Rondani); Yellow sugarcane aphid ( Yellow siskin Forbes); Wheat aphid ( Sitobion oats Fabricius); Alfalfa spotted aphid ( Therioaphis maculata Buckton; Citrus bifida ( Toxoptera orange Boyer de Fonscolombe and brown citrus aphid ( T. citricida Kirkaldy); Sugarcane aphid ( Sugar ants ); Coccidioides ( Adelges spp.); Walnut root phylloxera ( Phylloxera devastating Pergande); sweet potato whitefly ( Bemisia tabaci Gennadius, also known as tobacco whitefly; silver leaf whitefly ( B. silver-leafed Bellows & Perring); citrus whitefly ( Citrus citronellaAshmead; Winged whitefly ( Trialeurodes abutiloneus ) and greenhouse whiteflies ( T. vaporariorum Westwood; Potato leafhopper ( Bean sprouts Harris); Grey planthopper ( Laodelphax striatellus Fallen); Aster leafhopper ( Macrolestes quadrilineatus Forbes); Black-tailed Leafhopper ( Nephotettix cinquefoil Uhler); Electric Leafhopper ( N. nigropictus Stål); Brown planthopper ( Nilaparvata is mourning. Stål); Corn Lanternfly ( The pilgrim maid Ashmead; White-backed planthopper ( Sogatella furcifera Horvath; Rice-edge planthopper ( Sogatodes orizicola Muir); Apple leafhopper ( Typhlocyba pomaria McAtee); Leafhopper ( ) Erythroneura spp.); Seventeen-year cicada ( Seventeen Magicicada Linnaeus; cotton-shelled gecko ( Iceria purchased Maskell); Pear-shaped clams ( Quadraspidiotus pernicious Comstock); Citrus mealybug ( Planococcus citri Risso); *Pseudotailystomata* ( ) Pseudococci spp., and other mealybug complexes); pear psyllid ( Cacopsylla pyricola Foerster); persimmon psyllid ( Trioza diospyri Ashmead).

[0058] Species of interest with significant agronomic value in the order Hemiptera include, but are not limited to: green bugs (…). Acrosternum cheerful Say); Pumpkin-shaped stink bug ( Sad duck De Geer); White-winged long bug ( Bliss white-tailed eagle white-tailed eagle Say); Cotton lace bug ( Corythuca gossypii Fabricius); Tomato micropiper ( Cyrtopeltis modesta Distant); Cotton Red Bug ( Dysdercus suturellus Herrich-Schäffer); brown stink bug ( Euschistus, servant Say); Single-pointed bug ( E. variolarius Palisot de Beauvois; *Palaeotropium* ( ) Graptostethus spp., seed bug complex); Pine nut edge bug ( Leptoglossus corculus Say); Pasture mirid bug ( Lygus lineolaris Palisot de Beauvois; Western pasture mirid bug ( Lucius Hesperus Knight); Wild blind bug ( L. pratensis Linnaeus; European pasture mirid bug ( L. rugulipennis Poppius; Common green mirid bug ( Lygocoris pabulinus Linnaeus; Southern Green Bug ( Green-leaved nezara Linnaeus; Rice bug ( The fighting bull Fabricius); Giant long bug ( Oncopeltus fasciatus Dallas); cotton bollworm ( Pseudomoscelis seriatus Reuter).

[0059] Furthermore, the implementation plan may be effective for the following Hemiptera insects: the Norwegian blind bug (Lymantidae). Norwegian eel Gmelin); Field Miser Bug ( Orthops campestris Linnaeus; Apple-like blind bug ( Red-necked Plesiocoris Fallen); Tomato stink bug ( Cyrtopeltis modestus Distant); Tobacco micropiper ( Cyrtopeltis notatus Distant); Leukorrhea jumping mirid bug ( Spanagonicus albofasciatus Reuter); *Gnaphalium affine* (a type of mirid bug) Diaphnocoris chlorionis Say); Onion-shaped blind bug ( Labiopodidae garlic Knight); cotton bollworm ( Pseudomoscelis seriatus Reuter); Alfalfa mirid bug ( Adelphocoris rapidus Say); Four-lined mirid bug ( Poecilocapsus lineatus Fabricius); Small long bug ( Nysius heather Schilling); Radish bug ( Nysius radish Howard); Southern Green Bug ( Nezara greenish Linnaeus); genus *Linnaeus* (… Eurygaster spp.); species of the family Myxodidae ( Coreidae spp.); species of the red bug family ( Pyrrhocoridae spp.); species of the family Tenepiridae ( Tingidae spp.); species of the family Nephropidae ( Belostomatidae spp.); Species of the assassin bug family ( Reduviidae spp.) and species of the bedbug family ( Bedbugs spp.).

[0060] This also includes adult and larval mites of the order Acari (mites), such as the tulip gall mite (…). Acer small Keifer); Wheat mite ( Petrobia latentisMüller; spider mites and red spider mites in the family Tetranychidae, and the apple psyllid (M. mite). Panonychus elm Koch), two-spotted spider mite ( Spider mite Koch), McLeish spider mite ( T. mcdanieli McGregor, Carmine Tetranychus ( T. cinnabarinus Boisduval, Turkestan spider mite ( T. turkestani Ugarov & Nikolski; flat mite of the family Tenuipalpidae, short-haired mite of Liu's ( Brevipalpus lewisi McGregor; rust mites and bud mites of the family Eriophyidae, as well as other leaf-eating mites, and mites of significant importance to human and animal health, namely dust mites of the family Epidermoptidae, hair follicle mites of the family Demodicidae, and grain-eating mites of the family Glycyphagidae; ticks of the order Ixodidae, including the hard tick (Ixodidae). Ixodes scapularis Say), percyclic hard tick ( I. whole cycle Neumann, mutant tick ( Dermacentor variabilis Say), American abductor tick ( Amblyomma americanum Linnaeus; as well as scabies mites and itch mites from the families Psoroptidae, Pyemotidae, and Sarcoptidae.

[0061] Interesting insect pests of the order Thysanura, such as silverfish ( Sugar silverfish Linnaeus; Small silverfish ( Therobia domestica Packard).

[0062] Other arthropod insects covered include spiders in the order Araneae, such as the brown hermit spider (…). Loxosceles reclusa Gertsch and Mulaik) and Black Widow Spider ( Latrodectus mactans Fabricius; and myriapods in the order Scutigeromorpha, such as centipedes ( Scutigera coleoptrata Linnaeus).

[0063] Insects of interest include the superfamily Pentatomidae and other related insects, including but not limited to species belonging to the following families: Pentatomidae (Southern Green Bug) Nezara viridula ), tea-winged bug ( Halyomorpha halys ), the thread-seam red bug ( Piezodorus guildini ), brown bug ( Euschistus servus), Green bug ( Acrosternum hilare ), Hero Brown Bug ( Euschistus heroes ), Three-spotted brown bug ( Euschistus tristigmus ), forked bug ( Dichelops furcatus ), Black Horned Bug ( Dichelops melacanthus ) and the Gad bug ( Bagrada hilaris Plataspidae (including the sieve bean bug ( )); Megacopta cribraria )); and Cydnidae (chestnut-colored ground bug ( Scaptocoris castanea And Lepidoptera species, including but not limited to: diamondback moths, such as the American cotton bollworm ( Helicoverpa zea Boddie; soybean looper moth, for example, soybean looper moth ( Pseudoplusia includens Walker); and the bean moth, such as the bean moth (Walker); Anticarsia gemmatalis Hübner).

[0064] Nematodes include parasitic nematodes such as root-knot nematodes, cyst nematodes, and root-rot nematodes, including the genus *Heteroderm* (…). Heterodera spp.), root-knot nematodes ( Meloidogyne spp.) and spherical cyst nematodes ( Globodera spp.); especially members of cyst nematodes, including but not limited to: soybean cyst nematode ( Heterodera glycines ); beet cyst nematode ( Heterodera schachtii ); Oat cyst nematode ( Heterodera avenae ); and potato nematode ( Globodera rostochiensis ) and potato white nematode ( Globodera pallida Root-rot nematodes include the genus *Short-bodied Nematodes* (…). Pratylenchus (spp.). As used in this article, “insects” does not include nematodes.

[0065] Methods for measuring pesticide activity are well known in the art. See, for example, Czapla and Lang, (1990). J. Econ. Entomol. 83:2480-2485; Andrews et al., (1988) Biochem. J. 252:199-206; Marrone et al., (1985) J. of Economic Entomology References 78:290-293 and U.S. Patent No. 5,743,477, all of which are incorporated herein by reference in their entirety. Typically, proteins are mixed and used for feed assays. See, for example, Marrone et al., (1985). J. of Economic Entomology 78:290-293. Such determinations may include exposing a food source to one or more insects and determining the insects' viability.

[0066] Systems and methods are provided for sorting insect samples and preparing infection tests. Generally, insect samples may be provided in an unsorted configuration (e.g., unevenly distributed on a plate). Insect samples may be placed in a test tray. The test tray may, for example, include multiple cavities (e.g., optionally 24, 48, or 96 cavities), each cavity configured to receive one or more insect samples therein.

[0067] Using live insects (e.g., larvae) instead of eggs for infection can be advantageous because a) the unknown viability of eggs leads to uncertainty, and b) live larvae require less time to complete the experiment. Therefore, using live insects reduces the need for repeated experiments and the number of experimental trays used, thereby reducing experimental costs.

[0068] However, sorting live insects can be particularly difficult for several reasons. For example, live insects are easily moved. Therefore, it can be challenging to control insects on a board in an unsorted configuration. Furthermore, once placed on the experimental tray, insects may move from their intended location (e.g., within a specific cavity). Therefore, it is desirable to complete the sorting of insects on the experimental tray as quickly as possible before they can move to unwanted locations. Moreover, unlike infection using eggs, live insects move in an unsorted configuration, requiring techniques such as continuous image processing to identify the insects' current location for sorting.

[0069] Furthermore, the disclosed system and method can be used to sort eggs. It is conceivable that using eggs for infection could flexibly handle difficult larval species that produce excessive feces, filaments, or are sensitive to environmental conditions such as humidity.

[0070] The disclosed systems and methods can infect more mature larvae and other insects in the preparation process for experimental experiments. The disclosed systems and methods can handle multiple experiments simultaneously. The disclosed systems and methods can reduce costs. The disclosed systems and methods can allow for scalability based on output requirements based on changes (e.g., growth or shrinkage). The disclosed systems and methods allow for the integration of various other functions, such as leaf disc transfer. The disclosed systems and methods can reduce or eliminate ergonomic problems in artificial inoculation processes. The disclosed systems can be used for inoculation experiments on a variety of different species (e.g., twelve different species). The disclosed systems can be used to infect insects and more mature larvae than other inoculation methods.

[0071] The uses of the disclosed systems and methods are not limited to operations such as infection experiments. For example, the disclosed systems and methods can be used for insect rearing (e.g., moving arthropods from one container to another).

[0072] Other difficulties addressed by the disclosed systems and methods include the following: - The larvae can be very vulnerable. The viability of the larvae after infection is important to avoid affecting experimental data. If the larvae are injured, they may die prematurely, thus affecting the experimental results.

[0073] - The larvae may be very small - Traditional suction cups may not be an option - Small holes help prevent insects from crawling out of the experimental tray. The larvae are placed in experimental trays within hours of hatching. Their goal is to find food before dying. The larvae disperse rapidly within a short period and need to be controlled during any infection process.

[0074] This has driven the need for rapid filling and sealing of laboratory trays. - Efforts are needed to prevent the system itself from being compromised. - The larvae are sensitive to humidity and can become dehydrated in certain environments. Some larvae produce excessive amounts of filaments and feces, causing them to attach to tools when not in use. - Some larvae will bite through thin metal components. In some cases, larvae may clump together or stick together, forming larval balls that cannot be used for infection. In these respects, larvae can be divided into smaller groups or individual larvae. - There are more than a dozen species that share similarities and differences. The published system should ideally be versatile and capable of handling different species.

[0075] Insect eggs may stick together.

[0076] refer to Figure 1-3 The system 200 may include at least one sorting module 1. In various aspects, the system 200 may include multiple sorting modules 1. In other aspects, the system 200 may include only one sorting module 1. Each sorting module 1 may be configured to dispense insect samples onto an experimental plate, optionally from a tray on which insect samples are present.

[0077] In some aspects, system 200 may include a plate sealing assembly 3 configured to seal the experimental plate to contain insect samples within the plate. For example, the plate sealing assembly may use a membrane (e.g., a polymer membrane, and optionally, a polyester membrane such as Mylar). ® A polyester film was applied to the experimental plate.

[0078] The system may also include a puncture assembly 2 configured to puncture a membrane on a test plate. The puncture assembly 2 may be configured to puncture the test plate to form holes that allow air exchange but inhibit movement of insect samples through them. For example, in some aspects, the puncture assembly 2 is configured to form holes with diameters not exceeding 0.50 mm, 0.40 mm, 0.35 mm, 0.30 mm, or 0.25 mm. The puncture assembly 2 may be configured to interchangeably use different puncture elements that form holes of different sizes. In some optional aspects, the puncturator may be configured to simultaneously puncture multiple holes in the test plate. For example, the puncture assembly 2 may simultaneously hold multiple puncturators 290 (…). Figures 26B-27 In some respects, the puncture device 290 may be selected to form a predetermined number of holes (e.g., one hole, two holes, three holes, or more) to provide the required airflow. For example, different puncture devices may have different numbers of needles or different sizes of needles.

[0079] Figure 26A A conventional puncture device 290' is shown. The conventional puncture device is designed to fully open the cavities of the experimental plate, allowing other processes (e.g., fluid transfer) to enter the cavities. Figure 26B A puncture device 290 is further shown, configured for use with a conventional puncture assembly. The puncture device 290 may include a body 291 received within the puncture assembly. The puncture device 290 may also include a sleeve received around the body, wherein one or more needles 292 are secured between the body and the sleeve. See also... Figure 27 The puncture device 290 may include an outer body 294, which may be received in the puncture assembly 2. The outer body 294 may define a drill hole 295. The puncture device 290 may also include an inner fixation body 296, which may be received in the drill hole 295 of the outer body 294, wherein one or more needles 292 are fixed therein. The puncture device 290 may also include a cover biased relative to the tip of the needle, and fasteners may secure the cover to the inner fixation body 296. In some aspects, the inner fixation body 296 may define a slot to receive portions of the needle(s) 292(s). The inner fixation body 296 may define features (e.g., protrusions) that mate with corresponding features (e.g., grooves) to allow rotational alignment of the outer body 294 and the inner fixation body 296. It is conceivable that... Figure 27 The puncture device 290 shown in the diagram (with, for example) Figure 26B Compared to traditional puncture needles, they can be advantageously disassembled and reassembled for easy maintenance (e.g., when the needle is bent, dulled, damaged, or otherwise needs to be replaced).

[0080] System 200 may also include a service robot 15 configured to move objects within the system. For example, service robot 15 may transport a tray (e.g., a tray with insect samples on it) to and from sorting module 1. In a further aspect, the service robot may be configured to transport experimental panels to panel sealing assembly 3.

[0081] Sorting module refer to Figures 3-5 The sorting module 1 may include a receiving area 210 for receiving a tray 212 on which multiple insect samples are placed. The sorting module 1 may also include an experimental plate area 220 for receiving experimental plates 222.

[0082] In an exemplary aspect, the sorting module 1 may utilize image processing and a robot for picking up and placing insect samples. For example, in some aspects, the sorting module may also include a camera 230 configured to capture images of multiple insect samples on the tray when the tray is received within a receiving area, and a computing device (e.g., Figure 8A and Figure 9 The imaging computers 12 and 13 shown communicate with the camera 230. In an exemplary aspect, the camera 230 may be provided as an imaging assembly 7 ( Figure 8A As further disclosed herein, camera 230 and computing device may cooperate to provide image processing system 234, which is configured to identify the location of an individual insect sample among a plurality of insect samples on tray 212. It should be understood that the location of an individual insect sample may refer to a single, individual insect sample or the location of a single, individual insect sample within a group of insect samples.

[0083] The sorting module 1 may include a robot 240 configured to pick up an individual insect sample based on the corresponding location of the individual insect sample identified by the image processing system 234 and place the individual insect sample into an experimental plate 222 in the experimental plate area 220. In an exemplary aspect, the robot 240 may be implemented as a robotic arm 10, as further disclosed herein. The robot 240 may include, for example, a multi-axis robotic arm or a gantry.

[0084] In some aspects, the computing device of the image processing system 234 may be a dedicated image processing computing device. In other aspects, the computing device of the image processing system 234 may be operable to perform additional functions, including, for example, the control of the robot 240.

[0085] refer to Figure 5 and Figures 14-20 In some aspects, robot 240 may include at least one end effector 250 having an outlet 252. Robot 240 may be configured to apply a vacuum at the outlet 252 of at least one end effector 250 to pick up individual insect samples. For example, robot 240 may communicate with a vacuum pump or other vacuum source.

[0086] In some aspects, at least one end effector 250 may include a flow restrictor 254 configured to limit airflow through it to reduce the vacuum applied at outlet 252. For example, the flow restrictor may include a conduit 256 having a selected cross-sectional area. In other aspects, the flow restrictor may be defined by a borehole through the end effector 250. More generally, the flow restrictor 254 may be any structure that provides the desired vacuum cross-sectional area.

[0087] like Figure 14 and Figure 18 As shown, at least one end effector may also include a screen 258 positioned above the outlet of at least one end effector. The screen 258 may comprise, for example, sintered metal, wool, or paper. The screen 258 may be a filter to capture insect samples and prevent them from moving through the outlet 252. In some aspects, the end effector 250 may include a recess (e.g., an annular recess) to receive adhesive for adhering the screen across the outlet.

[0088] In some respects, robot 250 can also be configured to apply positive pressure across the outlet 252 of at least one end effector 250 to release individual insect samples. (Reference) Figures 18-20 At least one end effector may include a cover 260 configured to cover at least a portion of the upper opening of the cavity of the experimental plate 222. Figure 5 For example, in some aspects, the cover 260 may be configured to be received at least partially within the cavity. In other aspects, the cover 260 may be configured to extend over the upper opening of the cavity across the experimental plate. In an exemplary aspect, the cover 260 may include an annular member surrounding an outlet 252 that surrounds at least one end effector 250. In various optional aspects, the cover 260 of at least one end effector may include at least one groove 262 (e.g., a plurality of radially extending grooves) to allow gas to escape through it while inhibiting movement of individual insect samples through it. Thus, when a positive pressure is applied across the outlet 252 to release the insect sample, air can pass through the cover 250 without blowing the insect sample out of the cavity. Figure 20 As shown, the cover may include a port to allow excess adhesive to flow through, ensuring that the adhesive does not interfere with the operation of the end effector.

[0089] In some respects, robot 240 may have only a single end effector 250. Figure 24 In other respects, and such as Figures 21-23As shown, robot 240 may include a plurality of end effectors, provided as an end effector assembly. Optionally, in these aspects, robot 240 may be configured to vertically adjust the position of each end effector 250 relative to each of the other end effectors among the plurality of end effectors. For example, robot 240 may lower one end effector relative to the other end effectors to pick up and / or release each insect sample. In various aspects, the end effectors may be moved and positioned on corresponding tracks (e.g., via guide rails and carriages). Motor or pneumatic actuators may drive the vertical movement of end effector 250. For example, as shown, each end effector may include a corresponding cylinder to achieve the vertical movement of the end effector.

[0090] The end effector assembly may include an air manifold coupled to each end effector via a respective flexible channel conduit. In some aspects, the end effector may include a removable tip to allow for adaptability of the end effector 250 to different operations. The removable tip may be threaded for selectively coupling the tip to the end effector assembly.

[0091] It is conceivable that using multiple end effectors 252 can reduce contamination time. For example, in some aspects, the multiple end effectors 252 can be spaced apart along the axis. The spacing between adjacent end effectors can be the same as the spacing between adjacent cavities, so that the end effectors can be simultaneously aligned with the corresponding cavities of the experimental board 222.

[0092] In some aspects, the sorting module 1 may also include a robotic cleaning assembly configured to clean at least one end effector. The robotic cleaning assembly may include, for example, a spray nozzle, a brush, or a combination thereof. The robotic cleaning assembly may be positioned within the sorting module such that the robot can move the end effector to the robotic cleaning assembly. For example, in some aspects, the robotic cleaning assembly may include a brush, and the robot may move the end effector across the brush to remove any unwanted material (e.g., particulate matter, insect samples, etc.). Additionally or alternatively, as further disclosed herein, the end effector may exhaust air through outlet 252 to remove unwanted material from the end effector. It is conceivable that the exhaust operation may be automatically programmed into the robot's routine.

[0093] In some aspects, the sorting module 1 may include a leaf sample cutter configured to cut a portion of a leaf. In some aspects, the leaf sample cutter may position the leaf portion within a cavity in the experimental plate. In other aspects, the robot 240 may be configured to receive leaf samples and place them into the experimental plate. For example, Figure 16 The leftmost end effector 250 shown may have a sufficiently small diameter to be received within the cavity of the experimental plate, thus advantageously used for placing leaf samples (e.g., impellers) into the experimental plate. In other aspects, the robotic end effector may include a suction cup for grasping and transferring leaf samples.

[0094] refer to Figure 2 and Figure 25 In some embodiments, robot 240 may include at least one end effector 250' having an outlet 252'. Robot 240 may be configured to form a droplet at the outlet of the end effector (e.g., a pipette) sufficient to adhere to a single insect sample. The droplet may remain attached to the pipette. Robot 240 may also be configured to draw liquid into outlet 252' to draw a single insect sample therein. Robot 240 may also be configured to dispense the liquid containing the single insect sample into a test plate. Robot 240 may also be configured to use compressed air to remove any residual moisture at the outlet after insect dispensing to ensure that no amount of water or other liquid is present when the process is repeated.

[0095] In some exemplary aspects, robot 240 may include at least one end effector configured to selectively apply electrostatic charge to releasably hold a single insect sample thereon.

[0096] In some aspects, robot 240 may include at least one end effector comprising a brush configured to attach to an individual insect sample. The robot may also include an air source. An air blowing tube may be configured to receive air from the air source. The air blowing tube may have an outlet positioned to blow the individual insect sample off the brush when air is supplied to the air blowing tube by the air source.

[0097] In an exemplary aspect, the experimental board 220 may include multiple openings (e.g., recesses) configured to receive a predetermined number of insect samples. For example, the experimental board 220 may be used to measure insect samples (e.g., a single insect sample or a predetermined number of insect samples within each opening). A robot can receive insects from the predetermined multiple openings. In this way, the number of insect samples picked up each time can be easily managed. Furthermore, by controlling the insect samples within multiple openings, insect movement can be suppressed, thereby facilitating the process of picking up insect samples.

[0098] Insect control In various ways, the sorting module 1 can be configured to quench insect samples, for example, by using cooling and / or CO2. For example, see reference... Figures 11-12In some aspects, the sorting module may include a cooling system 280 configured to cool multiple insect samples received on a tray within a receiving area. Optionally, in these aspects, the cooling system 280 may include a cooler 282 located below the receiving area. The tray 212 or a portion thereof may have high thermal conductivity so that the cooler below the receiving area can cool the insect samples. For example, the tray 212 may include a base 214 with thermally conductive walls. The tray 212 may also include a tray insert 216 and multiple stand-offs 218 that space the tray insert 216 from the base 214 to provide thermal insulation gaps. It is conceivable that cooling only the periphery of the tray, rather than the area where the insects are located, can allow for a more even distribution of the insects. When insects are cooled, they typically move very slowly or not at all. Keeping the interior warmer helps allow for insect dispersion when insects begin to clump together or when the concentration is so high that the vacuum tip may tend to pick up more insects than expected. The cooled periphery of the tray can inhibit or prevent insects from crawling out of the tray and onto permanent machine components of the equipment.

[0099] In addition, the tray 212 may have a vibrating component attached thereto, thereby helping to disperse unevenly distributed insects. The surface of the tray 212 where the insects are located may also be patterned or textured to provide predictable insect distribution or prevent insects from gathering in unwanted areas of the tray.

[0100] refer to Figure 13 The receiving area 210 may include an inlet 270 for receiving gaseous dry ice. The receiving area 210 may also include a gas channel 272 covered by a heat-conducting wall 274. The gaseous dry ice may flow from the inlet 270 through the gas channel, thereby cooling the heat-conducting wall 274, and flow out from the outlet 276.

[0101] In some optional aspects, sorting module 1 ( Figure 1 This may include a CO2 dispenser configured to intermittently dispense CO2 as the tray is received within the receiving area. It is conceivable that intermittent dispensing could adequately sedate the insect samples without harming them.

[0102] In various further aspects, sorting module 1 ( Figure 1 Various features can be used to control insect samples on a tray. For example, refer to Figure 10 In some aspects, the sorting module may include an air curtain, which is configured to control insect samples on the tray when the tray is received within the receiving area. For example, tray 212 ( Figure 5The sample can be received within an area surrounded by a structure 302 that concentrates and guides the airflow. The structure 302 may include a peripheral channel 304 and a lip 306 that acts as a baffle to direct the airflow from the channel 304 to a desired air curtain direction (e.g., upwards, as shown). In a further aspect, the sorting module may include a vibrator configured to sufficiently vibrate the tray to suppress movement of the insect sample. For example, the sorting module may include a vibrating wall to suppress insects climbing the wall. In a further aspect, the tray may include a boundary defined by a color variation, configured to suppress movement of the insect sample across it. For example, the tray 212 may include a light-to-dark variation to define a boundary that suppresses the insect sample's tendency to move across. In a further aspect, lighting may be used to form the boundary that suppresses the movement of the insect sample across it. For example, lighting may be used to form extreme lighting variations to suppress the movement of the insect sample across it. In other aspects, attractive colors (e.g., yellow) may be used to suppress the insect sample from moving away from the desired location. In other aspects, the insect sample may be placed in shallow water. In other respects, insect samples can be placed in a container with one or more small holes to allow the insect samples to move through in a single file, such that as the insect samples move through the one or more small holes in sequence, they can be picked up by a robot for placement into an experimental plate. Image processing system 234 can be configured to detect each insect sample as each insect sample moves / appears from / through the one or more small holes.

[0103] In a further aspect, the tray may include a boundary defined by grease, configured to inhibit movement of insect samples across it. In some aspects, the tray may include a liquid trench configured to inhibit movement of insect samples across it. The liquid may be, or include, for example, water or alcohol.

[0104] calculate In some aspects, the computing device can be configured to apply blob detection to detect the corresponding location of an individual insect sample among multiple insect samples on a tray. In other aspects, the computing device can be configured to apply a machine learning algorithm to detect the corresponding location of an individual insect sample among multiple insect samples on a tray. Criteria for training the machine learning module may include one or more of the following, such as: size, shape, number of neighboring insects, location on the tray (e.g., preferentially picking insects closest to the edge to prevent them from condensing and being lost on the cooling tray wall), color for insect development purposes, and a preference for moving insects to ensure that the insect is alive.

[0105] The image processing system can be configured to provide real-time insect sample location data to accommodate insect sample movement. It is conceivable that the processing speed can be selected based on the insect's movement speed and the camera resolution. The camera resolution can be selected to provide sufficient image resolution to distinguish individual insects.

[0106] In some respects, the image processing system can be configured to determine the number of remaining insect samples on the tray in real time.

[0107] In some aspects, the computing devices of the image processing system 234 (e.g., imaging computers 11, 12) may be configured to coordinate with the programmable logic controller (PLC) of the robot 240 to coordinate the robot's motion and the actuation of the end effector. In other aspects, a single PLC may act as an imaging computer and control the robot. In these aspects, the sorting module 1 may include a PLC that coordinates all steps of picking up and placing. The PLC of the robot 240 may control the timing of the robot's pneumatic and vacuum components picking up and releasing insects. The PLC may also control the operation of the camera and associated lighting. In this way, image analysis can provide real-time object coordinates, thereby minimizing the delay between image capture and the use of image processing to pick up and place insect samples.

[0108] In some respects, the camera can be calibrated with the robot to utilize a common coordinate system. For example, a visual Cartesian coordinate system can be temporarily fixed below the camera within its field of view. The coordinate system can be imaged, and the captured image can be processed by algorithms to determine the origin, X-axis, and Y-axis of the coordinate system. While the coordinate system remains fixed and not allowed to move, the robot's end effector, when attached to the robot, can move (e.g., manually) to the origin, the endpoint of the X-axis, and then to the endpoint of the Y-axis. These points can be input into the robot's program using integrated teaching software. Thus, once completed, the camera and robot can learn the same coordinate system and work together as described herein.

[0109] Further exemplary sorting modules refer to Figure 28 The sorting module 1 may include an experimental plate area 220 for receiving experimental plates and an insect sample dispenser configured to dispense insect samples into the experimental plates within the experimental plate area. The insect sample dispenser may include a receiving space configured to receive multiple insect samples. The insect sample dispenser may also include a metering tray having multiple holes configured to receive corresponding insect samples from the multiple insect samples from the receiving space. The multiple holes are arranged and positioned above corresponding cavities of the experimental plate. For example, the multiple holes may have a spacing corresponding to the spacing between the cavities of the experimental plate. A sliding gate may be configured to slide relative to the metering tray to release insect samples from the multiple holes into the experimental plates within the experimental plate area.

[0110] In various respects, the upper chamber 310 may contain dry ice or CO2 gas that can be used to quench insect samples. The lower chamber 320 may contain insect samples. A baffle between the upper and lower chambers may control the temperature in the lower chamber and / or CO2 exposure in the lower chamber. The lower chamber 320 may include a metering tray with multiple holes.

[0111] In some respects, Figure 28 The components shown can be attached to automated devices, such as robots or gantry systems. Dry ice can be added to the top chamber 310, where sublimated CO2 gas permeates through holes into the middle chamber 330.

[0112] The gate 340 can be opened when a lower temperature is required (e.g., it retracts axially from the wall defining the boundary between the upper and middle chambers), thereby allowing CO2 gas to enter the middle chamber.

[0113] CO2 gas can be allowed to permeate through a pore extending between the middle and lower chambers.

[0114] Insects may be located in the lower chamber, where they are sedated by CO2 gas. A thermistor and / or oxygen sensor may be located in the lower chamber to monitor temperature or oxygen levels, allowing the lower chamber environment required by the insect species to be controlled using a gate 340. Multiple holes 350 at the bottom of the lower chamber may be aligned with the wells of the experimental plate to receive the insects. The holes 350 may be sized to accommodate a desired number of insect samples (e.g., larvae or eggs) entering the holes. This assembly may be shaken or abruptly moved to allow the sedated insect mass to fall into the holes (and to sweep across the holes when they are full of insect samples). The final location of any excess insects at this stage will be moved to either side of the lower chamber.

[0115] A sliding gate 360, located below orifice 350 and abutting the bottom outer surface of the lower chamber, prevents insects from falling into the orifices of the experimental plate. Only when the sliding gate is moved to the open position (when the orifice of the sliding gate 360 ​​is aligned with the orifice 350 at the bottom of the lower chamber 320) is the array of orifices in the sliding gate aligned with the lower orifice of the lower chamber and the orifice of the experimental plate, thus allowing the measured insects to fall into the orifices of the experimental plate. If desired, a slight increase in air pressure in the lower chamber can facilitate the ejection of the measured insects located within orifice 350 from the orifice aligned with the orifice of the experimental plate. A support plate below the sliding gate is fixed to the lower chamber, making it immovable relative to it. The support plate defines an array of orifices aligned with the orifice of the experimental plate to allow insect samples to fall into the orifice of the experimental plate. The support plate ensures that there is no gap between the orifice of the lower chamber and the sliding gate, thus preventing insect samples from passing through. The gap between the bottom of the support plate and the top of the experimental plate can be small or nonexistent to help ensure that the insect is not blown out of the experimental plate cavity if compressed air is used to push the insect out of the lower chamber hole.

[0116] In some optional aspects, the sorting module may include a robot that moves to fill multiple holes in the metering plate and remove excess insect samples that are not in the holes so that the insect samples can be accurately measured.

[0117] It is conceivable that various further sorting modules could be used in conjunction with sedated (e.g., with CO2) insects. For example, a sorting module could include a CO2 supply configured to sedate multiple insects, and a feeder configured to dispense the sedated insects.

[0118] refer to Figures 29A-30 In some aspects, the feeder may include (or be) a ladder feeder 400. The ladder feeder reciprocates the ladder steps (see...). Figure 29A and 29B The feeder moves a predetermined number (e.g., one, two, three, or more) of insect samples at a time to transport samples from the supply across the end shelf to the destination (e.g., the well of a test plate). In some aspects, the feeder may taper toward the end shelf to reduce the number of insect samples and return any fallen insect samples to the hopper.

[0119] refer to Figure 31 and Figure 32 In some aspects, the feeder may include (or be) a vibrating conveyor 450. A motor on the vibrating feeder is movably attached to two flexible legs of a trough. The legs may be shaped such that bending occurs along a single axis of motion. The trough may be designed and attached to the top of the legs. The angles of the legs and the trough may be selected based on the type of movement the insects experience within the trough. The trough moves at a high frequency and with low displacement to push the insects within the hopper. The hopper may be integrated into a V-shaped channel in which insects can be lined up. The vibrating feeder may cycle on and off as insects are needed. One or more insects may fall from the end of the V-shaped channel into the target experimental plate cavity. When the motor stops vibrating the hopper / trough section, no insects fall into the cavity. The frequency, amplitude, and duration of the vibration determine how many insects are placed into the experimental plate cavity below the end of the V-shaped channel.

[0120] In some aspects, the feeder may include a metering device configured to separate a predetermined number of insect samples from a plurality of insects. For example, the predetermined number of insect samples may be a single insect sample per cavity. In some aspects, the metering device may include a rotating wheel. The rotating wheel may define an opening configured to receive the predetermined number of insect samples (e.g., a single insect sample) therein.

[0121] In some aspects, the metering device may include a tray with multiple recesses configured to receive a predetermined number of insect samples. The tray may include multiple recesses arranged above corresponding cavities of a test plate. The sorting module may also include a conveyor configured to position the tray relative to the test plate so that the test plate is positioned above the tray. A rotating device may be configured to rotate the arrangement of the tray and the test plate such that a predetermined number of insect samples are poured from the multiple recesses of the tray into the corresponding cavities of the test plate. For example, the rotating device may be configured to simultaneously flip the tray and the test plate. In some aspects, the rotating device may include a clamp biased relative to a first side of the tray and an opposite side of the test plate to clamp the tray and the test plate together. In other aspects, the rotating device may grip each of the tray and the test plate separately and rotate the pair.

[0122] A method of using the sorting module disclosed herein may include dispensing at least one insect sample into a test plate. In some aspects, the at least one insect sample may include eggs. In some aspects, the at least one insect sample may include larvae.

[0123] In some aspects, the method may include tumbling an insect sample together with a plurality of particles, such that at least one insect sample adheres to a corresponding particle. At least one insect sample can be dispensed into a test plate by picking up a single particle from the plurality of particles on which at least one insect sample is attached.

[0124] In some aspects, the sorting module may include robots at both ends of the tube to control the position of each end of the tube. An inline vacuum generator allows insects to be picked up at one end of the tube and sucked through the tube into the experimental plate cavity at the other end of the tube.

[0125] In some aspects, the sorting module may include a multi-chamber tray into which CO2 is metered at different intervals. For example, the sorting module may include, Figures 11-12 The diagram shows an open cooling tray. Enclosed compartments can be provided on the cooling tray. Individual compartments can contain ambient air or a controlled amount of pumped-in CO2 gas. This allows individual compartments to be metered with CO2 for an appropriate time, then the sedated insects are picked up and placed into the experimental plate, while other sealed compartments contain air and active larvae. When the chamber being picked up is nearing depletion, the next chamber can be metered with CO2 to sedate the insects. When the initial chamber is depleted, the next chamber is opened. At this point, the insects in the second chamber are sedated and will not attempt to escape the input tray. Picking continues until most or all available insects have been picked up. Meanwhile, at the appropriate time, a third chamber can be metered with CO2 to prepare it for picking when the second chamber is depleted. The CO2 dosage can be programmed and executed by the machine. The advantages of this concept include: - Large numbers of insects can be loaded into the machine to reduce operator supervision time (allowing more attention to other tasks in the laboratory). - Large numbers of insects can be loaded into the machine with a lower risk of insects escaping from the input tray and contaminating the machine (better equipment hygiene and lower yield loss from input insects). Insects will not tolerate toxic doses of CO2 gas, only those optimized for their species. Larger individual compartments may require longer to deplete, otherwise some insects may become active and escape before the compartments are depleted. Large doses of CO2 gas can cause some insects to suffocate, resulting in insect yield loss, false data points from dead insects being placed in the wells, invalid replicates, wasted time, and wasted experimental materials and supplies. This can replace several smaller input trays that can be processed sequentially based on machine input. The smaller compartments can be several individual boxes, or a box with several individual compartments that can be placed into the machine simultaneously.

[0126] Exemplary aspects of machine learning As described in this paper, machine learning can be used to control the selection of insect samples placed in appropriate experimental panels. In some aspects, the selection can be binary, such as whether an insect sample should be placed in the experimental panel (e.g., by assessing whether the insect sample is alive or dead). In further aspects, the selection can include order, such as determining which insect samples should be picked up next. Computational devices (e.g., Figure 8A and Figure 9 The imaging computers 12 and 13 shown may include a machine learning module for processing the selection of insect samples placed on the respective experimental plates. Criteria for training the machine learning module may include one or more of the following: size, shape, number of neighboring insects, position on the tray (e.g., preferentially picking insects closest to the edge to prevent them from condensing and being lost on the cooling tray walls), color for insect development purposes, and a preference for moving insects to ensure that the insect is alive.

[0127] In an exemplary aspect, a computing device (e.g., Figure 8A and Figure 9 The imaging computers 12, 13 shown can use a segmentation model when analyzing images of one or more trays containing insect samples. The segmentation model can be the result of applying one or more machine learning models and / or algorithms to images associated with multiple trays containing insect samples. Machine learning is a subfield of computer science that enables computers to learn without explicit programming. Machine learning platforms include, but are not limited to, Naive Bayes classifiers, support vector machines, decision trees, neural networks, etc.

[0128] For example, a computing device may be used to receive and analyze multiple images associated with a tray containing insect samples, using one or more machine learning models and / or algorithms. Each of the multiple images associated with the tray containing insect samples may include one or more features. The multiple images may include a first portion (“first insect sample image”) and a second portion (“second insect sample image”). The second insect sample image may include insect sample images labeled as having (one or more) specific attributes or not having (one or more) specific attributes. The computing device (imaging computers 12, 13) may utilize one or more machine learning models and / or algorithms to determine those features (or vice versa) among the one or more features of the insect sample images that are most closely related to the image having (one or more) specific attributes (relative to the image not having (one or more) specific attributes). Using these closely related features, the computing device may generate a segmentation model. The segmentation model (e.g., a machine learning classifier) ​​may be generated to classify portions of the insect sample image as having (one or more) specific attributes based on the analysis of the pixels of the insect sample image.

[0129] As used herein, the term "segmentation" refers to the analysis of insect sample images to determine relevant insect sample image regions. In some cases, segmentation is based on the semantic content of the insect sample image. For example, segmentation analysis of an insect sample image may indicate insect sample image regions that depict one or more specific attributes of the corresponding insect sample. In some cases, segmentation analysis produces segmentation data. Segmentation data may indicate one or more segmented regions of the analyzed insect sample image. For example, segmentation data may include a set of labels, such as paired labels (e.g., labels with values ​​indicating "yes" or "no"), indicating whether a given pixel in the insect sample image belongs to a region that depicts one or more specific attributes of the corresponding insect sample image. In some cases, labels may have multiple available values, such as a set of labels indicating whether a given pixel (or pixels) depicts a first attribute, a second attribute, a combination of attributes, etc. Segmentation data may include numerical data, such as data indicating the probability that a given pixel (or pixels) belongs to a region that depicts one or more specific attributes of the corresponding insect sample image. In some cases, segmentation data may include additional types of data, such as text, database records, or additional data types or structures.

[0130] Furthermore, the segmentation model can be further characterized as an evaluation of the picking order. For example, a given pixel (or pixels) corresponding to the highest score in an insect sample image can be selected by robot 240 for picking. For example, proximity to the edge of the tray can help increase the picking score.

[0131] Turn now Figure 33The diagram illustrates system 800. System 800 can be configured to use machine learning techniques to train at least one machine learning-based classifier 830 by training module 820 based on analysis of one or more training datasets 810A-810B. Classifier 830 is configured to classify pixels in insect sample images as depicting or not depicting one or more specific attributes of the corresponding insect sample image. Training dataset 810A (e.g., a first portion of multiple images) may include labeled portions of the images (e.g., labeled as depicting or not depicting one or more specific attributes of the corresponding preferred insect sample). Training dataset 810B (e.g., a second portion of multiple images) may also include labeled imaging results (e.g., labeled as depicting or not depicting one or more specific attributes of the corresponding preferred insect sample). Labels may include "preferred insect sample" and "non-preferred insect sample".

[0132] The second portion of the multiple images can be randomly assigned to either the training dataset 810B or the test dataset. In some implementations, the assignment of data to the training or test dataset may not be entirely random. In this case, one or more criteria can be used during assignment, such as ensuring that each of the training and test datasets has a similar number of insect sample images with different labels. Generally, any suitable method can be used to assign data to the training or test dataset while ensuring that the distribution of sufficiently high-quality and insufficiently high-quality labels in the training and test datasets is substantially similar.

[0133] Training module 820 can train a machine learning-based classifier 830 by extracting a feature set from a first portion of multiple images in training dataset 810A according to one or more feature selection techniques. Training module 820 can also limit the feature set obtained from training dataset 810A by applying one or more feature selection techniques to a second portion of multiple images in training dataset 810B, the second portion including statistically salient features of positive examples (e.g., pixels depicting one or more specific attributes of the corresponding insect sample) and negative examples (e.g., pixels that do not depict one or more specific attributes of the corresponding insect sample).

[0134] Training module 820 can extract feature sets from training dataset 810A and / or training dataset 810B in various ways. Training module 820 can perform feature extraction multiple times, each time using a different feature extraction technique. In one implementation, feature sets generated using different techniques can each be used to generate different machine learning-based classification models 840. For example, the feature set with the highest quality metric can be selected for training. Training module 820 can use one or more feature sets to construct one or more machine learning-based classification models 840A-840N, which are configured to indicate whether a new image contains or does not contain pixels that depict one or more specific attributes of the corresponding insect sample.

[0135] Training datasets 810A and / or 810B can be analyzed to determine the dependencies, associations, and / or correlations between extracted features and sufficient / insufficient quality labels in training datasets 810A and / or 810B. The identified correlations can take the form of a list of features associated with labels of pixels depicting one or more specific attributes of the corresponding insect sample image and labels of pixels not depicting one or more specific attributes of the corresponding insect sample image. In the context of machine learning, these features can be considered variables. As used herein, the term "feature" can refer to any data item feature that can be used to determine whether a data item falls into one or more specific categories. For example, the features described herein may include one or more pixel attributes. One or more pixel attributes may include color saturation level, hue, contrast level, relative position, combinations thereof, etc. In other respects, one or more pixel attributes may be or include variations relative to another image, such as those used to determine insect movement.

[0136] Feature selection techniques may include one or more feature selection rules. These rules may include pixel attributes and pixel attribute occurrence rules. Pixel attribute occurrence rules may involve determining which pixel attributes in the training dataset 810A occur more than a threshold number of times, and identifying those pixel attributes that meet the threshold as candidate features. For example, any pixel attribute that occurs 8 or more times in the training dataset 810A may be considered a candidate feature. Any pixel attribute that occurs less than 8 times may be excluded from consideration as a feature. Any threshold amount may be used as needed.

[0137] Features can be selected using a single feature selection rule or multiple feature selection rules. Feature selection rules can be applied in a cascading manner, where they are applied in a specific order and applied to the results of the previous rule. For example, a pixel attribute occurrence rule can be applied to training dataset 810A to generate a first list of pixel attributes. The final list of candidate features can be analyzed using additional feature selection techniques to determine one or more candidate groups (e.g., pixel attribute groups). Any suitable computational technique can be used to identify candidate feature groups using any feature selection technique (e.g., filtering, wrapping, and / or embedded methods). One or more candidate feature groups can be selected using filtering methods. Filtering methods include, for example, Pearson correlation, linear discriminant analysis, analysis of variance (ANOVA), chi-square test, and combinations thereof. Feature selection using filtering methods is independent of any machine learning algorithm. Instead, features can be selected based on their relevance scores to the outcome variable in various statistical tests (e.g., pixels that depict or do not depict one or more specific attributes of the corresponding insect sample).

[0138] As another example, one or more candidate feature sets can be selected using a wrapper approach. A wrapper approach can be configured to use a subset of features and train a machine learning model using that subset. Based on inferences drawn from the previous model, features can be added and / or removed from that subset. Wrapper approaches include, for example, forward feature selection, backward feature elimination, recursive feature elimination, and combinations thereof. In one implementation, forward feature selection can be used to identify one or more candidate feature sets. Forward feature selection is an iterative method that begins with no features in the machine learning model. In each iteration, the features that best improve the model are added until adding new features no longer improves the performance of the machine learning model. In one implementation, backward elimination can be used to identify one or more candidate feature sets. Backward elimination is an iterative method that begins with all features in the machine learning model. In each iteration, the least significant features are removed until no improvement is observed after removing the features. Recursive feature elimination can be used to identify one or more candidate feature sets. Recursive feature elimination is a greedy optimization algorithm designed to find the best-performing subset of features. Recursive feature elimination repeatedly creates the model and retains the best or worst performing features in each iteration. Recursive feature elimination uses the remaining features to build the next model until all features are exhausted. The features are then sorted based on the order in which they were eliminated.

[0139] As a further example, one or more candidate feature groups can be selected using an embedded approach. Embedded approaches combine the advantages of filtering and wrapping methods. Examples of embedded approaches include, for instance, the Least Absolute Shrinkage and Selection Operator (LASSO) and Ridge Regression, which implement penalty functions to reduce overfitting. For example, LASSO regression performs L1 regularization, which adds a penalty term equal to the absolute value of the coefficients; while Ridge Regression performs L2 regularization, which adds a penalty term equal to the square of the coefficients.

[0140] After the training module 820 generates one or more feature sets, it can generate a machine learning-based classification model 840 based on the feature sets. The machine learning-based classification model can refer to a complex mathematical model for data classification generated using machine learning techniques. In one instance, the machine learning-based classifier may include support vector maps representing boundary features. For example, boundary features may be selected from the feature set and / or represent the highest-ranked features in the feature set.

[0141] Training module 820 can use feature sets extracted from training dataset 810A and / or training dataset 810B to construct machine learning-based classification models 840A-840N for each classification category (e.g., each attribute corresponding to an insect sample). In some instances, machine learning-based classification models 840A-840N can be combined into a single machine learning-based classification model 840. Similarly, machine learning-based classifier 830 can represent a single classifier containing one or more machine learning-based classification models 840, and / or multiple classifiers containing one or more machine learning-based classification models 840.

[0142] The extracted features (e.g., one or more pixel attributes) can be combined in a classification model trained using machine learning methods, such as discriminant analysis; decision trees; nearest neighbor (NN) algorithms (e.g., k-NN models, replicator NN models, etc.); statistical algorithms (e.g., Bayesian networks, etc.); clustering algorithms (e.g., k-means, mean-shift, etc.); neural networks (e.g., reservoir networks, artificial neural networks, etc.); support vector machines (SVM); logistic regression algorithms; linear regression algorithms; Markov models or chains; principal component analysis (PCA) (e.g., for linear models); multilayer perceptron (MLP) ANN (e.g., for nonlinear models); replicator reservoir networks (e.g., for nonlinear models, typically for time series); random forest classification; and combinations thereof. The resulting machine learning-based classifier 830 may include decision rules or mappings for each candidate pixel attribute to assign (e.g., depicting or not depicting (one or more) specific attributes of the corresponding insect sample).

[0143] Candidate pixel attributes and a machine learning-based classifier 830 can be used to predict the label (e.g., depicting or not depicting one or more specific attributes of the corresponding insect sample) of imaging outcomes in a test dataset (e.g., in a second part of multiple images). In one instance, the prediction for each imaging outcome in the test dataset includes a confidence level corresponding to the likelihood or probability that the corresponding pixel depicts or does not depict one or more specific attributes of the corresponding insect sample. The confidence level can be a value between 0 and 1, and it can represent the likelihood that the corresponding pixel belongs to a particular category. In one instance, when there are two states (e.g., depicting or not depicting one or more specific attributes of the corresponding insect sample), the confidence level can correspond to a value... p This refers to the probability that a particular pixel belongs to a first state (e.g., depicting (one or more) specific attributes). In this case, the value 1- p This can refer to the probability that a particular pixel belongs to a second state (e.g., not depicting (one or more) specific attributes). Generally, when there are more than two states, multiple confidence levels can be provided for each pixel and each candidate pixel attribute. The best-performing candidate pixel attribute can be determined by comparing the results obtained for each pixel with the known sufficient / insufficient quality states of each corresponding insect sample image in the test dataset (e.g., by comparing the results obtained for each pixel with labeled insect sample images in a second part of multiple images). Generally, the best-performing candidate pixel attribute among the specific attributes(one or more) of the corresponding insect sample will have results that closely match the known depicted / not depicted states.

[0144] The best-performing pixel attribute can be used to predict whether pixels in a new insect sample image are depicted or not. For example, a new insect sample image can be determined / accepted. The new insect sample image can be fed to a machine learning-based classifier 830, which can classify the pixels of the new insect sample image as depicted or not depicted based on the best-performing pixel attribute among one or more specific attributes of the corresponding insect sample.

[0145] The application may provide the computing device with instructions for one or more user edits to any attribute (or any created or deleted attribute) indicated by the segmentation mask. For example, a user may edit any attribute indicated by the segmentation mask by dragging certain points to the desired location via mouse movement to optimally depict the boundary description of attribute(s). As another example, a user may draw or redraw portions of the segmentation mask via mouse. Other input devices or methods of obtaining user commands may also be used. One or more user edits may be used by a machine learning module to optimize the semantic segmentation model. For example, training module 820 may extract one or more features from an output image containing one or more user edits as described above. Training module 820 may use one or more features to retrain the machine learning-based classifier 830, thereby continuously improving the results provided by the machine learning-based classifier 830.

[0146] A method for generating a machine learning-based classifier 830 can be used with training module 820. Training module 820 can implement supervised, unsupervised, and / or semi-supervised (e.g., reinforcement-based) machine learning-based classification models 840. The method shown is an example of a supervised learning method; variations of this training method example are discussed below; however, other training methods can be similarly implemented to train unsupervised and / or semi-supervised machine learning models.

[0147] The training method may determine (e.g., access, receive, retrieve, etc.) a first insect sample image (e.g., a first insect sample) associated with multiple insect samples and a second insect sample image (e.g., a second insect sample) associated with multiple insect samples. The first and second insect samples may each contain one or more imaging result datasets associated with the insect sample images, and each imaging result dataset may be associated with a specific attribute. Each imaging result dataset may include a list of labels for the imaging results. Labels may include "attribute pixels" and "non-attribute pixels".

[0148] The training method can generate training and testing datasets. These datasets can be generated by randomly assigning labeled imaging results from second insect sample images to either the training or testing dataset. In some implementations, the assignment of labeled imaging results to training or testing samples may not be entirely random. In one implementation, only labeled imaging results of a specific insect type and / or category can be used to generate the training and testing datasets. In another implementation, the majority of labeled imaging results of a specific insect type and / or category can be used to generate the training dataset. For example, 75% of the labeled imaging results of a specific insect type and / or category can be used to generate the training dataset, and 25% can be used to generate the testing dataset.

[0149] The training method may determine (e.g., extract, select, etc.) one or more features that can be used by, for example, a classifier to distinguish different categories (e.g., "attribute pixels" and "non-attribute pixels"). One or more features may include a set of imaging outcome attributes. In one embodiment, the training method may determine the feature set from a first insect sample image. In another embodiment, the training method may determine the feature set from a second insect sample image. In a further embodiment, the feature set may be determined from labeled imaging results of insect types and / or categories different from those associated with the labeled imaging results of the training dataset and the test dataset. In other words, labeled imaging results of different insect types and / or categories can be used for feature determination, rather than for training a machine learning model. The training dataset may be used in conjunction with labeled imaging results of different insect types and / or categories to determine one or more features. Labeled imaging results from different insect types and / or categories can be used to determine an initial feature set, which may be further reduced using the training dataset.

[0150] Training methods can use one or more features to train one or more machine learning models. In one implementation, supervised learning can be used to train the machine learning model. In another implementation, other machine learning techniques, including unsupervised and semi-supervised learning, can be employed. Depending on the problem to be solved and / or the available data in the training dataset, the machine learning model to be trained can be selected based on different criteria. For example, machine learning classifiers may suffer from varying degrees of bias. Therefore, more than one machine learning model can be trained and then optimized, improved, and cross-validated.

[0151] The training method may select one or more machine learning models to build a predictive model (e.g., a machine learning classifier). The predictive model can be evaluated using a test dataset. The predictive model can analyze the test dataset and generate categorical and / or predicted values. The categorical and / or predicted values ​​can be evaluated to determine if these values ​​achieve the desired level of accuracy.

[0152] The performance of the predictive models described in this paper can be evaluated in several ways based on the number of true positives, false positives, true negatives, and / or false negatives classified for pixels in an insect sample image. For example, a false positive for a predictive model can refer to the number of times the predictive model incorrectly classifies one or more pixels in an insect sample image as depicting a specific attribute when, in fact, the pixel does not depict that specific attribute. Conversely, a false negative for a machine learning model can refer to the number of times the predictive model classifies one or more pixels in an insect sample image as not depicting a specific attribute when, in fact, the one or more pixels do depict the specific attribute. True negatives and true positives can refer to the number of times the predictive model correctly classifies one or more pixels in an insect sample image as either fully depicting a specific attribute or not depicting a specific attribute. Related to these measurements are the concepts of recall and precision. Generally, recall is the ratio of true positives to the sum of true positives and false negatives, used to quantify the sensitivity of the predictive model. Similarly, precision is the ratio of true positives to the sum of true positives and false positives.

[0153] Sorting module 1 may include a verification imaging system that images the completed experimental board before it is removed. A camera equipped with a standard or telecentric lens can collect images and send them to a computing device for analysis of the completed experimental board images. An algorithm or machine learning algorithm used to analyze the images can determine whether each individual cavity within the experimental board contains the desired insects and pass this information to a robot controller to attempt to refill them.

[0154] Sorting module 1 can be configured to analyze the contents of each individual cavity within the experimental board and determine if any cavities are insufficient in number. This data can be passed to the sorting module robot to attempt to fill the insufficient cavities with the required number of insects, as many times as needed.

[0155] Exemplary aspects of the disclosed systems and methods In some embodiments, a method for sorting insects with high hatching rates is provided, including rinsing the insects with a rinsing solution; discarding floating insects from the rinsing solution; disinfecting the insects; separating immature insects from mature insects; and sorting mature insects using a sorting module. In another embodiment, a method for sorting insects with high hatching rates is provided, including removing insect clumps; hatching the insects until they show signs of development; and sorting mature insects using a sorting module.

[0156] In another embodiment, a method for determining insects is provided. In some embodiments, a method for determining insects is provided, comprising placing an insect sample (e.g., larvae or eggs) into a well of a measuring plate; capturing an image of the well of the measuring plate; and determining a metric measurement of the insect in the well of the measuring plate. In one embodiment, the metric measurement of the insect includes a pixel count using the image.

[0157] Another embodiment relates to a method for preparing insects for bioassays. In some embodiments, a method for preparing insects for bioassays is provided, including preparing individually dispersed insects and distributing a predetermined number of individually dispersed insects into each well of an assay plate.

[0158] A method for determining the activity of an insecticidal compound using an automated system is provided. In some embodiments, the provided method involves determining the activity of an insecticidal compound using an automated system, including providing a porous plate having at least one insect sample (e.g., larvae or eggs) in a predetermined number of wells; transporting the porous plate to a culture device for culture of the porous plate via an automated device; and transporting the porous plate to a measuring device via an automated device to measure movement or determine a metric measurement.

[0159] Methods for sorting insect eggs are provided. In some embodiments, a method for sorting insect eggs with a high hatching rate is provided, including rinsing the insect eggs with a rinsing solution; discarding floating insect eggs from the rinsing solution; disinfecting the insect eggs; separating immature insect eggs from mature insect eggs; and sorting mature insect eggs using a sorting module. In another embodiment, a method for sorting insect eggs with a high hatching rate is provided, including removing clumps of insect eggs; incubating the insect eggs until they show signs of development; and sorting mature insect eggs using a sorting module.

[0160] Another embodiment relates to a method for preparing insect eggs for bioassays. In some embodiments, a method for preparing insect eggs for bioassays is provided, including preparing individually dispersed insect eggs; selecting insect eggs ready for hatching within a predetermined time frame; and distributing a predetermined number of individually dispersed insect eggs into each well of the assay plate.

[0161] In one embodiment, the method of sorting insects includes sorting insect eggs. In another embodiment, the method of sorting insects includes sorting insect larvae. In a further embodiment, the method of sorting insects involves sorting both insect eggs and larvae.

[0162] In one embodiment, the method involving sorting insects includes selecting mature insect eggs. Mature insect eggs are insect eggs with a high probability of hatching within a predetermined time frame. In one embodiment, selecting mature insect eggs includes pre-treating the insects. In a further embodiment, the insect eggs are sorted by density gradient sorting and / or the sorting module 1 (or more sorting modules) further disclosed herein.

[0163] In one embodiment, the sorting module 1 may be capable of sorting using fluorescence, size, optical density, and side scattering parameters. In an exemplary embodiment, and with reference to... Figures 7-9The sorting module 1 may include a large particle sorting module (e.g., a large particle flow cytometer), as known in the art. In use, such a large particle sorting module can provide automated analysis and sorting of insect eggs and insects, as further disclosed herein. For example, the large particle sorting module may be able to sort objects by length, optical density, and fluorescence. In an exemplary configuration, the large particle sorting module may include a reservoir pressurized to create a constant fluid flow rate in a flow channel. Samples may be contained in a continuously mixed sample container and sufficiently pressurized to penetrate the laminar sheath flow, forming a core sample flow carried by the surrounding reservoir flow and positioning it at the center of the stream, where it may be irradiated by at least one visible laser (optionally, multiple lasers). The large particle sorting module may also include sensors or detectors for measuring various parameters, such as time-of-flight (signal length), optical density, and fluorescence emission, which can be analyzed as optical characteristics that can be used as sorting criteria. As fluid flows out of the flow channel, it may be deflected by an airflow to a recovery container. Alternatively, during the sorting operation, the airflow is shut off (to prevent fluid deflection), and fluid droplets containing sortable objects are dispensed through nozzles. In a further embodiment, the sorting module may include a stage configured to support a porous cavity plate receiving droplets from the dispensing nozzles. Objects deflected into a recovery container may be retrieved as needed for further evaluation and analysis. As further disclosed herein, the sorting module may also include a computer 120 configured to control the operation of the sorting module and may be loaded with software for performing data processing and sorting operations. In use, the large particle sorting module may be coupled to a large particle sampling component, as known in the art, which presents a sample to the large particle sorting module. An example of a large particle sorting module suitable for use as the sorting module disclosed herein is COPAS. ® (Union Biometrica, Inc., Holliston, MA) platform sorting module. In one embodiment, the sorting module selects eggs that are likely to hatch based on a pre-optimized range combination of green fluorescence, red fluorescence, and / or side-scattering readings.

[0164] In certain embodiments, the rinsing solution includes a bleach solution, an acidic solution, and / or an alcoholic solution. In one embodiment, the rinsing solution is a peracetic acid solution. In another embodiment, the rinsing solution is an ethanol solution. In yet another embodiment, rinsing may be performed sequentially, wherein each rinsing includes a different rinsing solution or the same rinsing solution.

[0165] In another embodiment, the method further includes the step of contaminating the test plate with insects. In another embodiment, the insects are sorted before contaminating the test plate. In a further embodiment, a sorting module 1 is used to contaminate the insects to contaminate the test plate. In this embodiment, the sorting module 1 may be a large-particle sorting module that sprays the insects into corresponding wells of the test plate. The test plate includes at least 2, at least 4, at least 6, at least 8, at least 12, at least 24, at least 48, at least 96, or at least 384 wells. In one embodiment, the test plate may be a microtiter plate. In one embodiment, the method involves contaminating one insect in each well. In another embodiment, the method involves contaminating more than one insect in each well. In one embodiment, the method involves contaminating a predetermined number of insects in equal amounts into multiple wells. In another embodiment, the wells contain a food source. In one embodiment, the test plate contains an artificial feed food source. In a further embodiment, the artificial feed food source is stained to prevent or adjust fluorescence emitted from the wells of the test plate. In one embodiment, the test plate contains live plant material. In a further embodiment, the cavity contains an insecticidal source. In one embodiment, the insecticidal source includes at least one of an insecticidal protein, an insecticidal silencing element or double-stranded RNA, or an insecticidal chemical substance.

[0166] In one embodiment, the method includes automatically infecting an insect into a test plate. In another embodiment, automatic infecting includes using a robotic arm to move the test plate into place for infecting, drying, sealing, or punching holes in the sealed test plate. In this embodiment, the robotic arm 10 may be a component of a larger robotic assembly that may include processing circuitry in communication with other system components as further disclosed herein. It is contemplated that the robotic arm 10 may include an end effector configured to selectively engage, orient, position, and disengage from the test plate as further disclosed herein. Optionally, the end effector may include a gripper, such as, but not limited to, an impact gripper (e.g., at least one claw or pincer), an adsorption gripper (e.g., a suction device), a contact gripper (e.g., a gripper with a surface containing adhesive or capable of applying surface tension or a freezing effect), or a combination thereof. It is further contemplated that the robotic arm 10 may include a plurality of links coupled together at joints to allow the links to rotate or translate axially relative to each other. Optionally, it is contemplated that the robotic arm 10 may be a multi-axis robotic arm with multiple degrees of freedom. For example, it is conceivable that a multi-axis robotic arm can be configured to move axially along multiple axes and rotate along at least one axis (optionally, multiple axes).

[0167] In one embodiment, the measuring plate includes a barcode. In this embodiment, the system may also include at least one barcode reader, as known in the art, which is communicatively coupled to a computer or other processing device as further disclosed herein. In one embodiment, the measuring plate is white, transparent, opaque, black, or other colored.

[0168] In another embodiment, a method for determining insects is provided, comprising placing an insect into a cavity of a measuring plate; capturing an image of the cavity of the measuring plate; and determining a measurement of the insect in the cavity of the measuring plate. In this embodiment, the placement of the insect into the cavity of the measuring plate can be performed using a sorting module as further disclosed herein. It is further envisioned that the image of the cavity of the measuring plate can be recorded using an imaging system, which may include at least one imaging component 7. Optionally, the imaging system may include a camera, microscope, X-ray device, magnetic resonance imaging (MRI) device, laser three-dimensional (“3-D”) scanner, and various other devices configured to generate images and identify the shape, pattern, orientation, color, and / or other characteristics of an object. In one embodiment, the measurement of the insect includes pixel counting using an image recorded by the imaging system. In another embodiment, the measurement includes measuring the fluorescence of the insect in the cavity of the plate. In one embodiment, the measurement includes detecting and / or recording the movement of the insect in the cavity of the measuring plate. Optionally, in this embodiment, the movement of the insect can be detected by a machine vision device. As used herein, machine vision refers to devices and methods for electronically identifying the shape, color, pattern, orientation, and / or other characteristics of an object using electronic sensing devices. In this regard, machine vision devices will generally be described herein as camera-based for the sake of brevity. However, in some embodiments, machine vision devices may include X-ray equipment, magnetic resonance imaging (MRI) equipment, laser three-dimensional (“3-D”) scanners, and various other devices configured to recognize the shape, pattern, orientation, color, and / or other characteristics of objects. Therefore, machine vision devices, alone or in combination with the processing devices described herein, can be used to detect insect movement as disclosed herein. Optionally, an imaging system as disclosed herein (e.g., at least one imaging component 7) may be used to detect insect movement. Thus, it is conceivable that the imaging system may include at least one camera capable of recording images and detecting movement as disclosed herein using processing circuitry as disclosed herein. Additionally or alternatively, motion sensors or detectors as known in the art may be used to detect and / or record insect movement.

[0169] In a further embodiment, detecting and / or recording movement includes aligning two or more images of an insect in a plate cavity at time intervals. In this embodiment, imaging software stored on imaging computers 130, 132, as further disclosed herein, can be used to align the two or more images. For example, such imaging software can be configured to generate an output corresponding to a visual superposition of discrete images captured at different times within the time interval, which is presented on a display device in communication with the processor of the imaging computer. In use, it is contemplated that the presented output can create a reference value that can be used to measure changes in movement (or area) over time. In another embodiment, the measurement includes measuring a metric. In this embodiment, it is contemplated that the metric can be determined using imaging software stored on a computer, as further disclosed herein, wherein the imaging software is configured to determine the size (e.g., body area) of the insect or a portion of the insect by processing previously captured images of the insect. In an exemplary embodiment, the body area measurement can be recorded in conjunction with other measurements (e.g., length, width, position, light intensity, size difference between time intervals). Additionally or alternatively, non-contact sensors capable of measuring size parameters (e.g., area, distance, length, etc.) can be used to determine the metric. Examples of such sensors include optical or laser sensors, instruments, or encoders as known in the art. However, it is conceivable that any known non-contact measurement sensor may be used. In use, area and position measurements can be used to generate insect response statistics, as well as other publicly available measurements for evaluating and determining insect responses. In one embodiment, a method for repeatedly placing an insect into a cavity of the measurement plate; capturing an image of the cavity; and determining the measurement of the insect in the cavity is repeated for each cavity in the measurement plate.

[0170] In another embodiment, a method involves bioassay, including sorting insects and determining IC-50, EC-50, or LC-50. In a further embodiment, a method involves bioassay, including sorting insects (e.g., using a sorting module as disclosed herein), capturing images of the insects (e.g., using a camera using an imaging system as disclosed herein), and determining IC-50, EC-50, or LC-50. In one embodiment, IC-50 or EC-50 is determined by an insect size measurement performed using imaging software or sensors as disclosed herein. In one embodiment, LC-50 is determined by a measurement of insect movement.

[0171] In one embodiment, the method involves determining the toxicity or insecticidal activity of a test substance, such as an insecticidal protein, an insecticidal silencing element, or double-stranded RNA, or a non-protein insecticidal chemical. In one embodiment, the test substance is a novel variant, a shuffled variant, or an insecticidal protein with domain exchanges. In another embodiment, the test protein is an unknown protein or a protein with unknown toxicity or insecticidal activity against insects. In a further embodiment, the assay uses a positive control insecticidal protein, wherein the toxicity of the positive control insecticidal protein is known. In one embodiment, the toxicity of the test protein is determined by determining the IC-50, EC-50, or LC-50 of the test protein.

[0172] In one implementation scheme, and with reference to Figures 7-9 The automated insect bioassay system may include at least one sorting module 1 as further disclosed herein (optionally, multiple sorting modules, such as first, second, and third sorting modules 1, 21, 22). Optionally, the bioassay system may also include at least one robotic arm 10 as further disclosed herein (optionally, multiple robotic arms 15, 16). Each robotic arm 10, 15, 16 may have its own processing circuitry configured to control the operation of the robotic arm and other system components as disclosed herein. Optionally, the processing circuitry of the robotic arm may include a central control (master) computer 11, 101 as disclosed herein.

[0173] Each sorting module 1, 21, 22 may have its own processing circuitry (e.g., computers 120, 122, 124 as disclosed herein), which is configured to allow selective control of the operation of the sorting module. In use, the processing circuitry of each sorting module may be communicatively coupled (e.g., integrated or wireless) to the processing circuitry of the robotic arm using conventional mechanisms (e.g., ActiveX control and / or serial port connections).

[0174] In another embodiment, the bioassay system may also include a plate sealing assembly 3, such as, but not limited to, the PlateLoc Thermal Microplate Sealer manufactured by Agilent Technologies, Inc. In one embodiment, as known in the art, the plate sealing assembly 3 may include a stage for receiving the assay plate, a holder for a roll of sealing material, a dispensing device for advancing and applying the sealing material at a selected amount and rate, and a user interface that allows control of sealing parameters (e.g., temperature, sealing time, etc.). In use, and as further disclosed herein, it is contemplated that the plate sealing assembly 3 may include processing circuitry communicatively coupled (e.g., integrated or wireless) to the processing circuitry of a robotic arm using conventional mechanisms (e.g., ActiveX controls and / or serial port connections).

[0175] In another embodiment, the automated insect bioassay system may also include a puncture assembly 2, such as, but not limited to, a microplate seal piercer manufactured by Agilent Technologies, Inc. As known in the art, the puncture assembly 2 may include a stage for receiving the assay plate, a puncture head configured for cyclical movement to puncture specific (e.g., selected or predetermined) portions of a seal previously applied to the assay plate, at least one actuator for implementing the cyclical movement of the puncture head, and a user interface coupled to the actuator and allowing control of the puncture operation. Optionally, the puncture head may define a plurality of protrusions that are generally vertically aligned with corresponding cavities of the assay plate during the puncture operation. In use, and as further disclosed herein, the puncture assembly 2 may be envisioned to include processing circuitry communicatively coupled (e.g., integrated or wireless) to processing circuitry of a robotic arm using conventional mechanisms (e.g., ActiveX control and / or serial port connections).

[0176] In another embodiment, the automated insect bioassay system may further include at least one imaging component 7 (optionally, first and second imaging components 7, 14), as disclosed herein. In this embodiment, the imaging component 7 may include a camera, microscope, sensor, or a combination thereof. The imaging component 7 may include a stage configured to receive and support the assay plate while the camera or microscope captures images of one or more cavities of the assay plate. The imaging component 7 may be communicatively coupled to processing circuitry that allows selective control of the operation of the imaging component (e.g., activation and image acquisition parameters). In use, the processing circuitry of the imaging component 7 may be communicatively coupled (e.g., integrated or wireless) to the processing circuitry of a robotic arm using conventional mechanisms (e.g., ActiveX control and / or serial port connections).

[0177] In another embodiment, the automated insect bioassay system may include an evaporator 4, such as, but not limited to, the ULTRAVAP microplate evaporator manufactured by Porvair Sciences. As is known in the art, the evaporator 4 may include a stage configured to receive and support the assay plate, a purging device (e.g., needle, pump, nozzle, etc.) located on the stage and capable of evaporating liquid within the corresponding cavities of the assay plate, and processing circuitry that allows selective control of the operation of the evaporator 4 (e.g., activation and drying parameters). In use, the processing circuitry of the evaporator 4 may be communicatively coupled (e.g., integrated or wireless) to the processing circuitry of a robotic arm using conventional mechanisms (e.g., ActiveX control and / or serial port connections).

[0178] In a further embodiment, the automated insect bioassay system may include at least one incubator 5 (optionally, multiple incubators, such as first and second incubators 5, 6), which may have a housing configured to receive at least one assay plate and devices configured to control various conditions within the housing (e.g., temperature, humidity, carbon dioxide content, oxygen content, etc.). In this embodiment, each incubator may also include processing circuitry communicatively coupled to the devices controlling the various conditions within the housing. In use, the processing circuitry of each incubator 5, 6 may be communicatively coupled (e.g., integrated connection or wireless connection) to the processing circuitry of a robotic arm using conventional mechanisms (e.g., ActiveX control and / or serial port connection).

[0179] In another embodiment, the automated insect bioassay system may further include at least one plate storage assembly 8, such as, but not limited to, the Labware MiniHub manufactured by Agilent Technologies, Inc. In this embodiment, the plate storage assembly may include at least one support shaft and multiple containers mounted along the length of the support shaft for receiving and supporting corresponding assay plates (or other laboratory equipment). The plate storage assembly 8 may also include a rotary actuator configured to perform rotation of the plate storage assembly 8, thereby providing access to selected assay plates stored on the plate storage assembly. The plate storage assembly 8 may also include processing circuitry communicatively coupled to the actuator to allow selective control of the rotational position of the plate storage assembly. Optionally, the automated insect bioassay system may include multiple plate storage assemblies, such as first and second storage assemblies 8, 17. In use, the processing circuitry of each plate storage assembly 8 may be communicatively coupled (e.g., integrated or wireless) to the processing circuitry of a robotic arm using conventional mechanisms (e.g., ActiveX control and / or serial port connections). It is contemplated that the plate storage in the first and second storage assemblies 8, 17 may be in a static position (e.g., unwired or wirelessly connected). In these respects, location can be encoded as available in the system's scheduling software.

[0180] In a further embodiment, the automated insect bioassay system may include at least one plate stacking assembly 9 (optionally, first and second plate stacking assemblies 23, 24), such as, but not limited to, the Labware Stacker manufactured by Agilent Technologies, Inc. In this embodiment, the plate stacking assembly 9 may include a vertical container housing multiple shelves that allow for the stacking and sequential distribution of assay trays. Optionally, the plate stacking assembly 9 may also include engagement devices (e.g., grippers, claws) configured to sequentially distribute individual assay trays from the stack of assay trays. The plate stacking assembly 9 may also include an unloading opening communicating with the vertical container to allow engagement between a robotic arm and the plates distributed by the plate stacking assembly. In use, the processing circuitry of the plate stacking assembly 9 (which may be configured to allow selective control of plate distribution) may be communicatively coupled (e.g., integrated or wireless) to the processing circuitry of the robotic arm using conventional mechanisms (e.g., ActiveX controls and / or serial port connections).

[0181] In a further embodiment, the automated insect bioassay system may include at least one barcode reader (e.g., first and second barcode readers 18, 19) located at a selected position within the system to allow tracking of the position of individual boards (as pre-labeled with barcodes as disclosed herein). Each barcode reader may include processing circuitry configured to transmit information about barcode detection and scanning (e.g., time, location, board identification, etc.). Optionally, each barcode reader may be communicatively coupled to a robotic arm. Optionally or alternatively, each barcode reader may be communicatively coupled to a host computer 11, 101 or a remote computing device 114a, 114b, 114c, as further disclosed herein.

[0182] By reference Figures 7-9 The example depicted illustrates the ability to construct an automated insect bioassay system using components fully compatible with the robotic system, where the processing circuitry of the robotic arm 10 (e.g., control (main) computers 11, 101) communicates with all system components and acts as the central processing unit for the entire system. In this example, it is conceivable that the robotic arm 10 could be located at the center of the system, with the remaining system components positioned around the robotic arm in a desired arrangement. In one embodiment, the stages and other accessible areas of the system components could be located in optimal radial positions relative to the robotic arm to ensure that these system component areas are easily accessible to the robotic arm as it extends radially. It is conceivable that such a configuration would allow for fully automated system operation.

[0183] In the sorting operation description provided herein, it is conceivable that all steps of the sorting process can be performed automatically. Where a specific structure for a step is not provided in the description, it should be understood that the step can be performed by the corresponding processing circuitry disclosed herein, which can automate the operation of system components or perform analysis.

[0184] In alternative embodiments, the methods and systems disclosed herein, in whole or in part, necessarily require the use of a machine, computer system, or equivalent apparatus, in which a set of instructions can be executed to cause the computer or machine to perform any one or more protocols or methods of the present invention. In alternative embodiments, the machine may be connected (e.g., networked) to other machines, such as via a local area network (LAN), intranet, extranet, or the Internet, or any equivalent network thereof. The machine may operate as a server or client in a client-server network environment, or as a peer in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), tablet computer, set-top box (STB), personal digital assistant (PDA), cellular phone, network device, server, network router, switch, or bridge, or any machine capable of executing a set of instructions (sequentially or otherwise) to specify the actions to be taken by the machine. The term "machine" should also be considered to include any collection of machines, computers, or articles of manufacture that individually or collectively execute a set of instructions (or multiple sets of instructions) to perform any one or more methods of the present invention.

[0185] In alternative implementations, and with reference to Figure 9 An exemplary computer system of the present invention includes a data network 140 and a communication network 150. In an exemplary embodiment, the data network 140 may include a control (main) computer 101 and at least one imaging computer (e.g., first and second imaging computers 12, 13) communicatively coupled to the control computer. The communication network 150 may include computers 120, 122, 124 for sorting modules 1, 21, 22 as disclosed herein. In one embodiment, sorting computers 120, 122, 124 may be communicatively coupled to the control computer 101 to transmit data obtained from the sorting modules and to allow control of the sorting modules via instructions received from the control computer.

[0186] The control computer 101 can operate in a network environment using a logical connection to one or more remote computing devices 114a, b, c. For example, the remote computing devices can be personal computers, laptops, smartphones, servers, routers, network computers, peer-to-peer devices, or other common network nodes. The logical connection between the control computer 101 and the remote computing devices 114a, b, c can be made via a network 115 (e.g., a local area network (LAN) and / or a general wide area network (WAN)). Such a network connection can be through a network adapter, which can be implemented in both wired and wireless environments. Optionally, such as... Figures 8A-8B As shown, the control computer 101 can operate as a host computer 11, providing processing circuitry for the robotic arm and being communicatively coupled to the robotic arm and other system components as further disclosed herein. The first and second imaging computers 12, 13 can also be connected to a remote computing device via network 115. Figures 8A-8B A non-limiting example of this configuration is shown, in which an automated insect bioassay system includes a main computer 11 and first and second imaging computers 12, 13. This example exemplifies a sorting module 1, a puncture assembly 2, a sealing assembly 3, an evaporator 4, a first incubator 5, a second incubator 6, a first imaging assembly 7, a first plate storage assembly 8, a main computer 11, a first imaging computer 12 (communically coupled to the first imaging assembly 7), a second imaging computer 13, a second imaging assembly 14 (communically coupled to the second imaging computer), a first robotic arm 15, a second robotic arm 16, a second plate storage assembly 17, a first barcode reader 18, a second barcode reader 19, a second sorting module 21, a third sorting module 22, a first stacking assembly 23, and a second stacking assembly 24.

[0187] In one exemplary configuration, the communication network 150 may be a LAN network that processes all scheduling instructions from the control computer 101 to the imaging computers 12, 13 (for reading) and the sorting computers 120, 122, 124 (for sorting). In operation, the imaging computers 12, 13 may be dedicated to image acquisition and transmitting large volumes of image data to the network 115, and the remote computing devices 114a, 114b, 114c may download image data and perform a series of image processing and statistical analyses. The communication network 150 may be independent and capable of operating independently of the data network 140.

[0188] Optionally, each computer disclosed herein may be envisioned to include its own processing unit (processor), system memory (which includes main memory (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM) etc.) and static memory (e.g., flash memory, static random access memory (SRAM) etc.) and data (mass) storage devices, which communicate with each other via a bus. System memory typically contains data, such as control processing data, and / or program modules, such as operating system and control processing software, which are readily accessible and / or currently operated on by the processing unit. Optionally, any number of program modules may be stored on the mass storage device, including, for example, operating system and control processing software. Each of the operating system and control processing software (or some combination thereof) may include a programming unit and a control processing software unit. Control processing data may also be stored on the mass storage device. Control processing data may be stored in any one or more databases known in the art. Examples of such databases include DB2®, Microsoft® Access, Microsoft® SQL. Server, Oracle®, MySQL, PostgreSQL, etc. Databases can be centralized or distributed across multiple systems.

[0189] In alternative embodiments, the processor of the computer disclosed herein represents one or more general-purpose processing devices, such as microprocessors, central processing units, etc. More specifically, the processor may be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, or a processor implementing other instruction sets or combinations of instruction sets. The processor may also be one or more special-purpose processing devices, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), network processors, etc. In alternative embodiments, the processor is configured to execute instructions (e.g., processing logic) to perform the operations and steps discussed herein.

[0190] In alternative implementations, the computer also includes a network interface device (adapter). The computer may also include a display device, which may be a video display unit (display device, such as a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer may also include a human-machine interface, which may include, for example, alphanumeric input devices (e.g., a keyboard), cursor control devices (e.g., a mouse), and signal generation devices (e.g., speakers). In addition to the human-machine interface, the computer may also include input / output interfaces.

[0191] In alternative embodiments, the data storage device (e.g., a drive unit) includes a computer-readable storage medium on which one or more instruction sets (e.g., software) embodying any one or more protocols, methods, or functions of the present invention are stored. The instructions may also reside wholly or at least partially in main memory and / or a processor during their execution, which also constitute machine-accessible storage media. The instructions may also be transmitted or received over a network via a network interface device.

[0192] In an alternative implementation, a computer-readable storage medium is used to store a set of data structures that define user identification states and user preferences (defining user profiles). The data structure set and user profiles may also be stored in other parts of the computer system, such as static memory.

[0193] In alternative embodiments, while the computer-readable storage medium in the exemplary embodiments is a single medium, the term "machine-accessible storage medium" can be considered to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store one or more sets of instructions. In alternative embodiments, the term "machine-accessible storage medium" can also be considered to include any medium capable of storing, encoding, or carrying a set of instructions that is executed by a machine and causes the machine to perform any one or more methods of the present invention. In alternative embodiments, the term "machine-accessible storage medium" should be accordingly considered to include, but is not limited to, solid-state memories as well as optical and magnetic media.

[0194] In alternative embodiments, information and signals may be presented using any techniques and / or skills known in the art. For example, data, instructions, commands, information, signals, bits, symbols, and chips used in practicing the components (apparatus, computers) and methods of the present invention may be presented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0195] In alternative embodiments, the various illustrative logic blocks, modules, circuits, and algorithmic steps used to describe exemplary embodiments of the invention can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps have been generally described above according to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the system as a whole. Those skilled in the art can implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0196] The algorithms and displays described herein are not inherently related to any particular computer or other device. Various general-purpose systems can be used with the teachings and procedures herein, or may prove convenient for building more specialized devices to perform the method steps. Furthermore, this disclosure does not refer to any particular programming language. In alternative embodiments, various programming languages ​​are used to implement the embodiments of the invention as described herein.

[0197] Exemplary aspects In view of the products, systems, and methods described herein and their variations, certain more specific aspects of the invention are described below. However, these specifically enumerated aspects should not be construed as having any limiting effect on any different claims that incorporate different or more general teachings described herein, or as limiting the “specific” aspects in some way other than the inherent meaning of the language used herein.

[0198] Aspect 1: A module comprising: The receiving area is used to receive a tray on which multiple insect samples are placed; An image processing system is configured to identify the location of an individual insect sample among multiple insect samples on a tray, the system having: A camera is configured to capture images of multiple insect samples on a tray when the tray is received within the receiving area; and A computing device that communicates with a camera; Experiment board area, used to receive experimental boards; and The robot is configured as follows: The individual insect sample is picked up based on the location of the individual insect sample identified by the image processing system; and The individual insect samples were placed into the experimental plates within the experimental plate area.

[0199] Aspect 2: The module according to aspect 1 also includes a cooling system configured to cool multiple insect samples received on a tray within the receiving area.

[0200] Aspect 3: The module according to aspect 2, wherein the cooling system includes a cooler under the receiving area.

[0201] Aspect 4: The module according to aspect 3 further includes a tray received in the receiving area, wherein at least a portion of the tray has high thermal conductivity.

[0202] Aspect 5: The module according to any one of the preceding aspects further includes a CO2 dispenser configured to intermittently dispense CO2 when the tray is received in the receiving area.

[0203] Aspect 6: The module according to any one of the preceding aspects further includes an air curtain configured to control the insect sample on the tray when the tray is received in the receiving area.

[0204] Aspect 7: The module according to any one of the preceding aspects further includes a vibrator configured to sufficiently vibrate the tray to suppress movement of the insect sample.

[0205] Aspect 8: The module according to any one of the preceding aspects further includes a tray received within a receiving area, wherein the tray includes a boundary defined by a color change, the boundary being configured to inhibit movement of the insect sample across the boundary.

[0206] Aspect 9: The module according to any one of the preceding aspects further includes a tray received within a receiving area, wherein the tray includes a boundary defined by grease, the boundary being configured to inhibit movement of the insect sample across it.

[0207] Aspect 10: The module according to any one of the preceding aspects further includes a tray received in a receiving area, wherein the tray includes a liquid trench configured to inhibit movement of insect samples across it.

[0208] Aspect 11: The module according to aspect 10, wherein the liquid trench comprises water or alcohol.

[0209] Aspect 12: The module according to any one of the preceding aspects further includes a tray received in a receiving area, wherein the tray includes components that induce vibrational movement to evenly disperse insects on the tray.

[0210] Aspect 13: According to the module described in aspect 12, the surface of the tray is textured to facilitate the desired distribution of insects upon vibration.

[0211] Aspect 14: A module according to any one of the preceding aspects, wherein the robot includes at least one end effector having an outlet, wherein the robot is configured to apply a vacuum at the outlet of at least one end effector to pick up individual insect samples.

[0212] Aspect 15: According to the module of aspect 14, at least one end effector includes a flow restrictor configured to limit the airflow passing through it to reduce the area of ​​the vacuum effect applied at the outlet.

[0213] Aspect 16: The module according to aspect 15, wherein the flow limiter includes a pipe having a selected cross-sectional area.

[0214] Aspect 17: The module according to any one of aspects 14-16, wherein at least one end effector includes a screen located at at least one end effector outlet.

[0215] Aspect 18: A module according to any one of aspects 14-17, wherein at least one end effector is further configured to apply positive pressure to release a single insect sample.

[0216] Aspect 19: According to the module of aspect 18, at least one end effector includes a cover configured to cover at least a portion of the upper opening of the experimental plate cavity.

[0217] Aspect 20: The module according to aspect 19, wherein the cover includes an annular member surrounding at least one end effector outlet.

[0218] Aspect 21: According to the module of aspect 20, the cover of at least one end effector includes at least one groove that allows gas to pass through and be discharged while inhibiting the movement of individual insect samples passing through.

[0219] Aspect 22: A module according to any one of aspects 14-21, wherein at least one end effector comprises a plurality of end effectors.

[0220] Aspect 23: The module according to aspect 22, wherein the robot is configured to vertically adjust the position of each end effector relative to each of the plurality of other end effectors.

[0221] Aspect 24: The module according to any one of aspects 14-23 further includes a robot cleaning component configured to clean at least one end effector.

[0222] Aspect 25: The module according to aspect 24, wherein the robotic cleaning component includes a spray nozzle, a brush, or a combination thereof.

[0223] Aspect 26: A module according to any one of aspects 1-11, wherein the robot includes at least one end effector having an outlet, wherein the robot is configured to form droplets at the outlet of the end effector sufficient to attach to a single insect sample.

[0224] Aspect 27: According to the module described in aspect 26, the robot is further configured to draw liquid into an outlet to draw in individual insect samples.

[0225] Aspect 28: According to the module described in aspect 27, the robot is further configured to dispense liquid containing individual insect samples into the experimental plate.

[0226] Aspect 29: According to the module described in aspect 28, the robot is further configured to discharge any remaining external fluid with compressed air.

[0227] Aspect 30: A module according to any one of aspects 1-11, wherein the robot includes at least one end effector configured to selectively apply electrostatic charge to releasably hold a single insect sample thereon.

[0228] Aspect 31: A module according to any one of aspects 1-11, wherein the robot includes at least one end effector, said end effector comprising: A brush, which is configured to attach to individual insect samples; Gas source; and An air blowing tube is configured to receive air from an air source and has an outlet positioned to blow individual insect samples off a brush when the air source supplies air to the air blowing tube.

[0229] Aspect 32: The module according to any one of the preceding aspects, wherein the computing device is configured to apply spot detection to detect the location of an individual insect sample among a plurality of insect samples on a tray.

[0230] Aspect 33: The module according to any one of the preceding aspects, wherein the computing device is configured to apply a machine learning algorithm to detect the location of an individual insect sample among a plurality of insect samples on a tray.

[0231] Aspect 34: The module according to any one of the preceding aspects, wherein the image processing system is configured to provide real-time insect sample location data to accommodate the movement of the insect sample.

[0232] Aspect 35: The module according to any one of the preceding aspects, wherein the image processing system is configured to provide real-time determination of the number of remaining insect samples on the tray.

[0233] Aspect 36: The module according to any one of the preceding aspects further includes a leaf cutter configured to cut a portion of a leaf and position the leaf portion within a cavity of the experimental plate.

[0234] Aspect 37: The module according to any one of the preceding aspects, wherein the experimental board includes a plurality of openings configured to receive a predetermined number of insect samples, wherein the robot is configured to receive insects from the predetermined plurality of openings.

[0235] Aspect 38: A module comprising: The experimental board area for receiving experimental boards; and An insect sample dispenser configured to dispense insect samples into an experimental plate within an experimental plate area, the insect sample dispenser comprising: A receiving space, configured to receive multiple insect samples; A metering tray having multiple holes configured to receive corresponding insect samples from a plurality of insect samples from a receiving space, wherein the multiple holes are arranged to be positioned above the corresponding cavities of the experimental plate; A sliding gate, configured to slide relative to a metering tray, releases insect samples from multiple wells into an experimental plate within the experimental plate area.

[0236] Aspect 39: A module comprising: The CO2 supply was configured to sedate multiple insects; and A feeder, configured to dispense calmed insects.

[0237] Aspect 40: The module according to aspect 39, wherein the feeder includes a ladder feeder.

[0238] Aspect 41: The module according to aspect 36, wherein the feeder includes a vibrating conveyor.

[0239] Aspect 42: The module according to aspect 36, wherein the feeder includes a metering device configured to separate a predetermined number of insect samples from a plurality of insects.

[0240] Aspect 43: The module according to aspect 42, wherein the predetermined number of insect samples is a single insect sample.

[0241] Aspect 44: The module according to aspect 42, wherein the measuring device includes a rotating wheel, wherein the rotating wheel defines an opening configured to receive a predetermined number of insect samples therein.

[0242] Aspect 45: The module according to aspect 42, wherein the metering device includes a tray having a plurality of openings configured to receive a predetermined number of insect samples.

[0243] Aspect 46: According to the module of aspect 45, the tray includes a plurality of openings arranged above the corresponding cavities of the experimental plate.

[0244] Aspect 47: The module according to aspect 46 further includes: A conveyor, configured to position the experimental plates above a tray; and A rotating device is configured to rotate the arrangement of a tray and an experimental plate, such that a predetermined number of insect samples are poured from multiple openings in the tray into corresponding cavities in the experimental plate.

[0245] Aspect 48: A system comprising: At least one module according to any one of the foregoing aspects; and A service robot configured to transport a pallet to and from the at least one module.

[0246] Aspect 49: The system according to aspect 48 further includes a sealer configured to seal the experimental plate to contain the insect sample within the experimental plate, wherein a service robot is configured to transport the experimental plate to the sealer.

[0247] Aspect 50: The system according to any one of aspects 48-49 further includes a puncturist configured to puncture the experimental plate to form a hole that allows air exchange but inhibits movement of the insect sample through it.

[0248] Aspect 51: The system according to aspect 50, wherein the puncture device is configured to form a hole with a diameter not exceeding 0.35 mm.

[0249] Aspect 52: The system according to aspect 50, wherein the puncturist is configured to simultaneously puncture multiple holes in the experimental plate.

[0250] Aspect 53: The system according to aspect 50, wherein the puncture device is configured to interchangeably use different puncture elements forming holes of different sizes.

[0251] Aspect 54: The system according to aspect 50, wherein at least one module comprises a plurality of modules according to any one of the preceding aspects.

[0252] Aspect 55: A method comprising: At least one insect sample is dispensed into the experimental plate using the module according to any one of aspects 1-47.

[0253] Aspect 56: According to the method of aspect 55, at least one insect sample is an egg or larva.

[0254] Aspect 57: The method according to aspect 55 further includes tumbling the insect sample together with a plurality of particles such that at least one insect sample is attached to a corresponding particle, wherein distributing at least one insect sample into the experimental plate includes picking up individual particles from the plurality of particles on which at least one insect sample is attached.

[0255] Although the above embodiments of the present invention have been described in some detail by way of illustration and examples for clarity of understanding, certain variations and modifications are included within the scope of the appended claims.

Claims

1. A module comprising: a receiving area for receiving a tray having a plurality of insect samples thereon; an image processing system configured to identify locations of individual insect samples in the plurality of insect samples on the tray, the system having: a camera configured to capture images of the plurality of insect samples on the tray while the tray is received within the receiving area; and a computing device in communication with the camera; a plate area for receiving a plate; and a robot configured to: pick up an individual insect sample based on the location of the individual insect sample identified by the image processing system; and place the individual insect sample into the plate of the plate area.

2. The module of claim 1, further comprising a cooling system configured to cool the plurality of insect samples on the tray received within the receiving area.

3. The module of claim 2, wherein the cooling system comprises a chiller below the receiving area.

4. The module of claim 3, further comprising the tray received within the receiving area, wherein at least a portion of the tray has high thermal conductivity.

5. The module of claim 1, further comprising a CO2 dispenser configured to intermittently dispense CO2 while the tray is received within the receiving area.

6. The module of claim 1, further comprising an air curtain configured to contain the insect samples on the tray while the tray is received within the receiving area.

7. The module of claim 1, further comprising a vibrator configured to substantially vibrate the tray to inhibit movement of the insect samples.

8. The module of claim 1, further comprising the tray received within the receiving area, wherein the tray comprises a border defined by a color change, the border configured to inhibit movement of the insect samples across the border.

9. The module of claim 1, further comprising the tray received within the receiving area, wherein the tray comprises a border defined by a grease, the border configured to inhibit movement of the insect samples across the border.

10. The module of claim 1, further comprising the tray received within the receiving area, wherein the tray comprises a liquid moat, the moat configured to inhibit movement of the insect samples across the moat.

11. The module of claim 10, wherein the liquid moat comprises water or alcohol.

12. The module of claim 1, further comprising the tray received within the receiving area, wherein the tray comprises a component that induces vibrational motion to uniformly disperse the insects on the tray.

13. The module of claim 12, wherein a surface of the tray is textured to facilitate achieving a desired distribution of the insects when vibrated.

14. The module of claim 1, wherein the robot comprises at least one end effector having an outlet, wherein the robot is configured to apply a vacuum at the outlet of the at least one end effector to pick up the individual insect sample.

15. The module of claim 14, wherein the at least one end effector comprises a flow restrictor configured to restrict airflow therethrough to reduce a vacuum footprint applied at the outlet.

16. The module of claim 15, wherein the flow restrictor comprises a tube having a selected cross-sectional area.

17. The module of claim 14, wherein the at least one end effector comprises a screen positioned above the at least one end effector exit.

18. The module of claim 14, wherein the at least one end effector is further configured to apply positive pressure to release the individual insect sample.

19. The module of claim 18, wherein the at least one end effector comprises a cover configured to cover at least a portion of an upper opening of a well of a plate.

20. The module of claim 19, wherein the cover comprises a ring that surrounds the at least one end effector exit.

21. The module of claim 20, wherein the cover of the at least one end effector comprises at least one groove that allows gas to pass therethrough for expulsion while inhibiting movement of the individual insect sample therethrough.

22. The module of claim 14, wherein the at least one end effector comprises a plurality of end effectors.

23. The module of claim 22, wherein the robot is configured to vertically adjust a position of each end effector relative to each other end effector of the plurality of end effectors.

24. The module of claim 14, further comprising a robotic cleaning assembly configured to clean the at least one end effector.

25. The module of claim 24, wherein the robotic cleaning assembly comprises a spray nozzle, a brush, or a combination thereof.

26. The module of claim 1, wherein the robot comprises at least one end effector having an exit, wherein the robot is configured to form a droplet at the exit of the end effector sufficient to adhere to the individual insect sample.

27. The module of claim 26, wherein the robot is further configured to draw liquid into the exit to draw the individual insect sample therein.

28. The module of claim 27, wherein the robot is further configured to dispense the liquid with the individual insect sample therein into a plate.

29. The module of claim 28, wherein the robot is further configured to expel any remaining external fluid with compressed air.

30. The module of claim 1, wherein the robot comprises at least one end effector configured to selectively apply an electrostatic charge to releasably hold the individual insect sample thereon.

31. The module of claim 1, wherein the robot comprises at least one end effector comprising: a brush configured to adhere to the individual insect sample; a gas source; and a blow tube configured to receive air from the gas source, the blow tube having an exit positioned to blow the individual insect sample off the brush when the gas source supplies air to the blow tube.

32. The module of claim 1, wherein the computing device is configured to apply blob detection to detect a location of the individual insect sample of the plurality of insect samples on the tray.

33. The module of claim 1, wherein the computing device is configured to apply a machine learning algorithm to detect a location of the individual insect sample of the plurality of insect samples on the tray. ​ 34. The module of claim 1, wherein the image processing system is configured to provide real-time insect sample location data to accommodate insect sample movement.

35. The module of claim 1, wherein the image processing system is configured to provide real-time determination of the number of insect samples remaining on the tray.

36. The module of claim 1, further comprising a leaf sample cutter configured to cut a portion of a leaf and position the leaf portion within a well cavity of the experimental plate.

37. The module of claim 1, wherein the experimental plate comprises a plurality of openings configured to receive a predetermined number of insect samples, wherein the robot is configured to receive insects from the predetermined plurality of openings.

38. A module comprising: an experimental plate region to receive an experimental plate; and an insect sample dispenser configured to dispense insect samples into an experimental plate within the experimental plate region, the insect sample dispenser comprising: a receiving space configured to receive a plurality of insect samples; a metering tray having a plurality of holes configured to receive respective insect samples from the plurality of insect samples from the receiving space, wherein the plurality of holes are arranged to be positioned over respective well cavities of the experimental plate; a sliding shutter configured to slide relative to the metering tray to release the insect samples within the plurality of holes into the experimental plate within the experimental plate region.

39. A module comprising: a CO2 supply configured to sedate a plurality of insects; and a feeder configured to dispense the sedated insects.

40. The module of claim 39, wherein the feeder comprises a ladder feeder.

41. The module of claim 36, wherein the feeder comprises a vibratory conveyor.

42. The module of claim 36, wherein the feeder comprises a metering device configured to isolate a predetermined number of insect samples from the plurality of insects.

43. The module of claim 42, wherein the predetermined number of insect samples is a single insect sample.

44. The module of claim 42, wherein the metering device comprises a rotating wheel, wherein the rotating wheel defines an opening configured to receive the predetermined number of insect samples therein.

45. The module of claim 42, wherein the metering device comprises a tray having a plurality of openings configured to receive the predetermined number of insect samples.

46. The module of claim 45, wherein the tray comprises a plurality of openings arranged to be positioned over respective well cavities of the experimental plate.

47. The module of claim 46, further comprising: a conveyor configured to position the experimental plate over the tray; and a rotating device configured to rotate the arrangement of the tray and the experimental plate such that the predetermined number of insect samples fall from the plurality of openings of the tray into the respective well cavities of the experimental plate.

48. A system comprising: at least one module according to any one of claims 1-37; and a service robot configured to transport the tray to and from the at least one module. ​ ​ ​ ​ 49. The system of claim 48, further comprising a sealer configured to seal the experimental plate to confine the insect sample within the experimental plate, wherein the service robot is configured to transport the experimental plate to the sealer.

50. The system of claim 48, further comprising a piercer configured to pierce the experimental plate to form holes that allow for air exchange but inhibit movement of the insect sample therethrough.

51. The system of claim 50, wherein the piercer is configured to form holes that are no more than 0.35 mm in diameter.

52. The system of claim 50, wherein the piercer is configured to pierce multiple holes in the experimental plate simultaneously.

53. The system of claim 50, wherein the piercer is configured to interchangeably use different piercing elements that form holes of different sizes.

54. The system of claim 50, wherein the at least one module comprises a plurality of modules according to any of the preceding claims.

55. A system comprising: at least one module according to any of claims 39-47; and a service robot configured to transport the tray to and from the at least one module.

56. A method comprising: dispensing at least one insect sample into an experimental plate using a module according to any of claims 1-37.

57. The method of claim 56, wherein the at least one insect sample is an egg or a larva.

58. The method of claim 56, further comprising tumblering the insect sample with a plurality of particles such that the at least one insect sample adheres to a respective particle, wherein dispensing the at least one insect sample into the experimental plate comprises picking up an individual particle of the plurality of particles having the at least one insect sample adhered thereto.

59. A method comprising: dispensing at least one insect sample into an experimental plate using a module according to any of claims 39-47. ​

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