Methods and systems for sorting and imaging insects

Abstract

A module for sorting insects includes a receiving area for receiving a tray having thereon a plurality of insect samples. A camera is configured to capture images of the plurality of insect samples on the tray when received within the receiving area. A computing device is in communication with the camera. The camera and computing device cooperate to define an image processing system that is configured to identify a location of an individual insect sample of the plurality of insect samples on the tray. The module comprises a lab plate area for receiving a lab plate. A robot is configured to pick up the 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 lab plate in the lab plate area.

Description

[0001] Attorney Docket: 36446.0365P1 METHODS AND SYSTEMS FOR SORTING AND IMAGING INSECTS CROSS-REFERENCE TO RELATED APPLICATION This application claims priority to and the benefit of the filing date of U.S. Provisional Patent Application No.63 / 517,497, filed August 3, 2023, the entirety of which is incorporated by reference herein. FIELD The present embodiments of the invention generally relate to methods and systems for sorting and imaging insects, including egg and larval life stages, useful for automated high throughput bioassays. BACKGROUND There has been a long felt need for compositions and methods for controlling or eradicating insect pests of agricultural significance. There is also a long felt need for high throughput methods and systems of screening candidate compositions and methods and systems for controlling or eradicating insect pests of agricultural significance. BRIEF SUMMARY Systems and methods are provided for sorting insects. In some embodiments, a module for sorting insects includes a receiving area for receiving a tray having thereon a plurality of insect samples. A camera is configured to capture images of the plurality of insect samples on the tray when received within the receiving area. A computing device is in communication with the camera. The camera and computing device cooperate to define an image processing system that is configured to identify a location of an individual insect sample of the plurality of insect samples on the tray. The module comprises a lab plate area for receiving a lab plate. A robot is configured to pick up the 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 lab plate in the lab plate area. In some embodiments, a module includes a lab plate area for receiving a lab plate and an insect sample dispenser that is configured to dispense insect samples into the lab plate within the lab plate area. The insect sample dispenser includes a receiving space that is configured to receive a plurality of insect samples. A metering tray has a plurality of holes that are configured to receive respective insect samples of the plurality of insect samples from the receiving space. The plurality of holes are arranged to be positioned above respective wells of the lab plate. Attorney Docket: 36446.0365P1 A slide gate can slide relative to the metering tray to release the insect sample within the plurality of holes into the lab plate within the lab plate area. In some embodiments, a module includes a CO2 supply that is configured to sedate a plurality of insects and a feeder that is configured to distribute the sedated insects. A system includes a module for sorting insect samples and a service robot that is configured to transport the tray to and from the module Methods of using the modules and systems are also disclosed. DESCRIPTION OF FIGURES FIG.1 shows a perspective view of a non-limiting example of an automated insect bioassay system. FIG.2 shows a top view of the automated insect bioassay system of FIG.1. FIG.3 shows a top view of a sorting module of the automated insect bioassay system of FIG.1. FIG.4 shows a side view of the sorting module of the automated insect bioassay system of FIG.1. FIG.5 shows a sectional view of the sorting module of the automated insect bioassay system of FIG.4, taken in the plane A-A of FIG.4. FIG.6 shows a non-limiting example of an automated insect bioassay system from a top view. The example illustrates a sorting module, a piercing assembly, a sealing assembly, an evaporator, a first incubator, an imaging assembly, a plate storage assembly, a plate stacking assembly, and a robotic arm. FIG.7 shows the non-limiting example of an automated insect bioassay system of FIG.6 from a side view. The example illustrates a sorting module, a piercing assembly, a sealing assembly, an evaporator, a first incubator, a second incubator, an imaging assembly, a plate storage assembly, and a plate stacking assembly. FIG.8A shows a second non-limiting example of an automated insect bioassay system from a top view. The example illustrates a first sorting module, a piercing assembly, a sealing assembly, an evaporator, a first incubator, a second incubator, a first imaging assembly, a first plate storage assembly, a master computer, a second general computer, a third general 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. FIG.8B shows the second non-limiting example of an automated insect bioassay system from a side view. The example illustrates a sorting module, a piercing assembly, a Attorney Docket: 36446.0365P1 sealing assembly, an evaporator, a first incubator, a second incubator, a first imaging assembly, a master computer, a second general computer, and first and second plate stacking assemblies. FIG.9 is a schematic diagram depicting an exemplary arrangement of a computer system for use with an automated insect bioassay system as disclosed herein. FIG.10 is a sectional perspective view of an assembly for providing an air curtain. FIG.11 is an exploded view of a cooling system. FIG.12 is an exploded view of a tray of the cooling system of FIG.11. FIG.13 is a perspective view of a portion of a cooling system as disclosed herein. FIG.14 is a perspective view of portions of a plurality of end effectors of a robot of the sorting module as disclosed herein, showing different screen / filter types. FIG.15 is a perspective view of an exemplary end effector of the robot of the sorting module as disclosed herein. FIG.16 is a perspective view of a plurality of exemplary end effectors of the robot of the sorting module as disclosed herein. FIG.17 shows sectional views of plurality of exemplary end effectors of FIG.16. FIG.18 shows a cross sectional view of an exemplary end effector of the robot of the sorting module as disclosed herein. FIG.19 shows a perspective view of the exemplary end effector of FIG.18. FIG.20 shows a perspective view of an exemplary end effector of the robot of the sorting module as disclosed herein. FIG.21 shows a perspective view of an exemplary end effector assembly of the robot of the sorting module as disclosed herein, the exemplary end effector assembly comprising a plurality of independently articulable end effectors. FIG.22 shows a side view of the exemplary end effector assembly of FIG.21. FIG.23 shows a front view of the exemplary end effector assembly of FIG.21. FIG.24 shows an exemplary end effector assembly of the robot of the sorting module as disclosed herein, the exemplary end effector assembly having a single end effector. FIG.25 shows an exemplary end effector assembly of the robot of the sorting module as disclosed herein. FIG.26 shows a perspective view of a plurality of exemplary piercing elements. FIG.27 shows an exploded view of an exemplary piercing element. FIG.28 shows a sectional view of an exemplary sorting assembly as disclosed herein. Attorney Docket: 36446.0365P1 FIG.29A shows a side schematic view of a ladder feeder in a first configuration. FIG.29B shows a side schematic view of the ladder feeder of FIG.29A in a second position. FIG.30 shows a top view of the ladder feeder of FIGS.29A-B. FIG.31 shows a top view of an exemplary vibratory feeder. FIG.32 shows a side view of the vibratory feeder of FIG.31. FIG.33 is a block diagram of an exemplary system configured to use machine learning as disclosed herein. DETAILED DESCRIPTION The embodiments of the invention are not limited by the exemplary methods and materials disclosed, and any methods and materials similar or equivalent to those described can be used in the practice or testing of embodiments of this invention. Numeric ranges are inclusive of the numbers defining the range. Unless context dictates otherwise, the articles “a” and “an” are used to refer to one or more than one (i.e., to at least one) of the grammatical object of the article. For example, “an element” can refer to one or more elements. As used herein “high hatch rate” is intended to mean a hatch rate of insect eggs of at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 100%. As used herein, “insect” refers to all life stages of an insect or any one life stage of an insect, including but not limited to, eggs and larvae. As used herein, “IC-50” or inhibition concentration, and “EC-50” or effective concentration each may be used interchangeably, and refers to the concentration at which the larvae size (as may be determined by the larvae pixel area) is half way between the maximum size (the zero dose control), and the smallest size (the most 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). As used herein, removing insect clumps includes, but is not limited to, clearing, dissolving, disintegrating, manually or mechanically separating clumping, aggregations or clustering of insects. Insect clumps may comprise insect eggs and / or insect larvae. In one embodiment, removing insect clumps comprises using a sieve. In another embodiment, removing insect clumps comprises using enzymatic or chemical breakdown of proteins holding the insects together, for example digesting peptides holding eggs together. Attorney Docket: 36446.0365P1 In one embodiment of the invention, a method is provided for sorting insects having a high hatch rate comprising rinsing insects with a rinse solution; discarding floating insects from the rinse solution; sterilizing the insects; separating immature insects from mature insects; and sorting the mature insects in using a sorting module. In another embodiment, a method is provided for sorting insects having a high hatch rate comprising removing insect clumps after incubating the insect eggs; incubating the insects until the insects show signs of development; and sorting the mature insects using a sorting module. In another embodiment, insects that are not viable or insect eggs that are not likely to hatch are discarded. In some embodiments, the methods are useful using insects selected from the orders Coleoptera, Diptera, Hymenoptera, Lepidoptera, Mallophaga, Homoptera, Hemiptera Orthroptera, Thysanoptera, Dermaptera, Isoptera, Anoplura, Siphonaptera, Trichoptera, etc., particularly Lepidoptera and Coleoptera. Larvae of the order Lepidoptera include, but are not limited to, armyworms, cutworms, loopers and heliothines in the family Noctuidae: Spodoptera frugiperda JE Smith (fall armyworm); S. exigua Hübner (beet armyworm); S. litura Fabricius (tobacco cutworm, cluster caterpillar); Mamestra configurata Walker (bertha armyworm); M. brassicae Linnaeus (cabbage moth); Agrotis ipsilon Hufnagel (black cutworm); A. orthogonia Morrison (western cutworm); A. subterranea Fabricius (granulate cutworm); Alabama argillacea Hübner (cotton leaf worm); Trichoplusia ni Hübner (cabbage looper); Pseudoplusia includens Walker (soybean looper); Anticarsia gemmatalis Hübner (velvetbean caterpillar); Hypena scabra Fabricius (green cloverworm); Heliothis virescens Fabricius (tobacco budworm); Pseudaletia unipuncta Haworth (armyworm); Athetis mindara Barnes and Mcdunnough (rough skinned cutworm); Euxoa messoria Harris (darksided cutworm); Earias insulana Boisduval (spiny bollworm); E. vittella Fabricius (spotted bollworm); Helicoverpa armigera Hübner (American bollworm); H. zea Boddie (corn earworm or cotton bollworm); Melanchra picta Harris (zebra caterpillar); Egira (Xylomyges) curialis Grote (citrus cutworm); borers, casebearers, webworms, coneworms, and skeletonizers from the family Pyralidae Ostrinia nubilalis Hübner (European corn borer); Amyelois transitella Walker (naval orangeworm); Anagasta kuehniella Zeller (Mediterranean flour moth); Cadra cautella Walker (almond moth); Chilo suppressalis Walker (rice stem borer); C. partellus, (sorghum borer); Corcyra cephalonica Stainton (rice moth); Crambus caliginosellus Clemens (corn root webworm); C. teterrellus Zincken (bluegrass webworm); Cnaphalocrocis medinalis Guenée (rice leaf roller); Desmia funeralis Hübner (grape leaffolder); Diaphania hyalinata Linnaeus (melon worm); D. nitidalis Stoll (pickleworm); Diatraea grandiosella Dyar Attorney Docket: 36446.0365P1 (southwestern corn borer), D. saccharalis Fabricius (surgarcane borer); Eoreuma loftini Dyar (Mexican rice borer); Ephestia elutella Hübner (tobacco (cacao) moth); Galleria mellonella Linnaeus (greater wax moth); Herpetogramma licarsisalis Walker (sod webworm); Homoeosoma electellum Hulst (sunflower moth); Elasmopalpus lignosellus Zeller (lesser cornstalk borer); Achroia grisella Fabricius (lesser wax moth); Loxostege sticticalis Linnaeus (beet webworm); Orthaga thyrisalis Walker (tea tree web moth); Maruca testulalis Geyer (bean pod borer); Plodia interpunctella Hübner (Indian meal moth); Scirpophaga incertulas Walker (yellow stem borer); Udea rubigalis Guenée (celery leaftier); and leafrollers, budworms, seed worms and fruit worms in the family Tortricidae Acleris gloverana Walsingham (Western blackheaded budworm); A. variana Fernald (Eastern blackheaded budworm); Archips argyrospila Walker (fruit tree leaf roller); A. rosana Linnaeus (European leaf roller); and other Archips species, Adoxophyes orana Fischer von Rösslerstamm (summer fruit tortrix moth); Cochylis hospes Walsingham (banded sunflower moth); Cydia latiferreana Walsingham (filbertworm); C. pomonella Linnaeus (coding moth); Platynota flavedana Clemens (variegated leafroller); P. stultana Walsingham (omnivorous leafroller); Lobesia botrana Denis & Schiffermüller (European grape vine moth); Spilonota ocellana Denis & Schiffermüller (eyespotted bud moth); Endopiza viteana Clemens (grape berry moth); Eupoecilia ambiguella Hübner (vine moth); Bonagota salubricola Meyrick (Brazilian apple leafroller); Grapholita molesta Busck (oriental fruit moth); Suleima helianthana Riley (sunflower bud moth); Argyrotaenia spp.; Choristoneura spp.. Selected other agronomic insects in the order Lepidoptera include, but are not limited to, Alsophila pometaria Harris (fall cankerworm); Anarsia lineatella Zeller (peach twig borer); Anisota senatoria J.E. Smith (orange striped oakworm); Antheraea pernyi Guérin- Méneville (Chinese Oak Tussah Moth); Bombyx mori Linnaeus (Silkworm); Bucculatrix thurberiella Busck (cotton leaf perforator); Colias eurytheme Boisduval (alfalfa caterpillar); Datana integerrima Grote & Robinson (walnut caterpillar); Dendrolimus sibiricus Tschetwerikov (Siberian silk moth), Ennomos subsignaria Hübner (elm spanworm); Erannis tiliaria Harris (linden looper); Euproctis chrysorrhoea Linnaeus (browntail moth); Harrisina americana Guérin-Méneville (grapeleaf skeletonizer); Hemileuca oliviae Cockrell (range caterpillar); Hyphantria cunea Drury (fall webworm); Keiferia lycopersicella Walsingham (tomato pinworm); Lambdina fiscellaria fiscellaria Hulst (Eastern hemlock looper); L. fiscellaria lugubrosa Hulst (Western hemlock looper); Leucoma salicis Linnaeus (satin moth); Lymantria dispar Linnaeus (gypsy moth); Manduca quinquemaculata Haworth (five spotted hawk moth, tomato hornworm); M. sexta Haworth (tomato hornworm, tobacco Attorney Docket: 36446.0365P1 hornworm); Operophtera brumata Linnaeus (winter moth); Paleacrita vernata Peck (spring cankerworm); Papilio cresphontes Cramer (giant swallowtail orange dog); Phryganidia californica Packard (California oakworm); Phyllocnistis citrella Stainton (citrus leafminer); Phyllonorycter blancardella Fabricius (spotted tentiform leafminer); Pieris brassicae Linnaeus (large white butterfly); P. rapae Linnaeus (small white butterfly); P. napi Linnaeus (green veined white butterfly); Platyptilia carduidactyla Riley (artichoke plume moth); Plutella xylostella Linnaeus (diamondback moth); Pectinophora gossypiella Saunders (pink bollworm); Pontia protodice Boisduval and Leconte (Southern cabbageworm); Sabulodes aegrotata Guenée (omnivorous looper); Schizura concinna J.E. Smith (red humped caterpillar); Sitotroga cerealella Olivier (Angoumois grain moth); Thaumetopoea pityocampa Schiffermuller (pine processionary caterpillar); Tineola bisselliella Hummel (webbing clothesmoth); Tuta absoluta Meyrick (tomato leafminer); Yponomeuta padella Linnaeus (ermine moth); Heliothis subflexa Guenée; Malacosoma spp. and Orgyia spp. Of interest are larvae and adults of the order Coleoptera including weevils from the families Anthribidae, Bruchidae and Curculionidae (including, but not limited to: Anthonomus grandis Boheman (boll weevil); Lissorhoptrus oryzophilus Kuschel (rice water weevil); Sitophilus granarius Linnaeus (granary weevil); S. oryzae Linnaeus (rice weevil); Hypera punctata Fabricius (clover leaf weevil); Cylindrocopturus adspersus LeConte (sunflower stem weevil); Smicronyx fulvus LeConte (red sunflower seed weevil); S. sordidus LeConte (gray sunflower seed weevil); Sphenophorus maidis Chittenden (maize billbug)); flea beetles, cucumber beetles, rootworms, leaf beetles, potato beetles and leafminers in the family Chrysomelidae (including, but not limited to: Leptinotarsa decemlineata Say (Colorado potato beetle); Diabrotica virgifera virgifera LeConte (western corn rootworm); D. barberi Smith and Lawrence (northern corn rootworm); D. undecimpunctata howardi Barber (southern corn rootworm); Chaetocnema pulicaria Melsheimer (corn flea beetle); Phyllotreta cruciferae Goeze (Crucifer flea beetle); Phyllotreta striolata (stripped flea beetle); Colaspis brunnea Fabricius (grape colaspis); Oulema melanopus Linnaeus (cereal leaf beetle); Zygogramma exclamationis Fabricius (sunflower beetle)); beetles from the family Coccinellidae (including, but not limited to: Epilachna varivestis Mulsant (Mexican bean beetle)); chafers and other beetles from the family Scarabaeidae (including, but not limited to: Popillia japonica Newman (Japanese beetle); Cyclocephala borealis Arrow (northern masked chafer, white grub); C. immaculata Olivier (southern masked chafer, white grub); Rhizotrogus majalis Razoumowsky (European chafer); Phyllophaga crinita Burmeister (white grub); Ligyrus gibbosus De Geer (carrot beetle)); carpet beetles from the Attorney Docket: 36446.0365P1 family Dermestidae; wireworms from the family Elateridae, Eleodes spp., Melanotus spp.; Conoderus spp.; Limonius spp.; Agriotes spp.; Ctenicera spp.; Aeolus spp.; bark beetles from the family Scolytidae and beetles from the family Tenebrionidae. Adults and immatures of the order Diptera are of interest, including leafminers Agromyza parvicornis Loew (corn blotch leafminer); midges (including, but not limited to: Contarinia sorghicola Coquillett (sorghum midge); Mayetiola destructor Say (Hessian fly); Sitodiplosis mosellana Géhin (wheat midge); Neolasioptera murtfeldtiana Felt, (sunflower seed midge)); fruit flies (Tephritidae), Oscinella frit Linnaeus (fruit flies); maggots (including, but not limited to: Delia platura Meigen (seedcorn maggot); D. coarctata Fallen (wheat bulb fly) and other Delia spp., Meromyza americana Fitch (wheat stem maggot); Musca domestica Linnaeus (house flies); Fannia canicularis Linnaeus, F. femoralis Stein (lesser house flies); Stomoxys calcitrans Linnaeus (stable flies)); face flies, horn flies, blow flies, Chrysomya spp.; Phormia spp. and other muscoid fly pests, horse flies Tabanus spp.; bot flies Gastrophilus spp.; Oestrus spp.; cattle grubs Hypoderma spp.; deer flies Chrysops spp.; Melophagus ovinus Linnaeus (keds) and other Brachycera, mosquitoes Aedes spp.; Anopheles spp.; Culex spp.; black flies Prosimulium spp.; Simulium spp.; biting midges, sand flies, sciarids, and other Nematocera. Included as insects of interest are adults and nymphs of the orders Hemiptera and Homoptera such as, but not limited to, adelgids from the family Adelgidae, plant bugs from the family Miridae, cicadas from the family Cicadidae, leafhoppers, Empoasca spp.; from the family Cicadellidae, planthoppers from the families Cixiidae, Flatidae, Fulgoroidea, Issidae and Delphacidae, treehoppers from the family Membracidae, psyllids from the family Psyllidae, whiteflies from the family Aleyrodidae, aphids from the family Aphididae, phylloxera from the family Phylloxeridae, mealybugs from the family Pseudococcidae, scales from the families Asterolecanidae, Coccidae, Dactylopiidae, Diaspididae, Eriococcidae Ortheziidae, Phoenicococcidae and Margarodidae, lace bugs from the family Tingidae, stink bugs from the family Pentatomidae, cinch bugs, Blissus spp.; and other seed bugs from the family Lygaeidae, spittlebugs from the family Cercopidae squash bugs from the family Coreidae and red bugs and cotton stainers from the family Pyrrhocoridae. Agronomically important members from the order Homoptera further include, but are not limited to: Acyrthisiphon pisum Harris (pea aphid); Aphis craccivora Koch (cowpea aphid); A. fabae Scopoli (black bean aphid); A. gossypii Glover (cotton aphid, melon aphid); A. maidiradicis Forbes (corn root aphid); A. pomi De Geer (apple aphid); A. spiraecola Patch (spirea aphid); Aulacorthum solani Kaltenbach (foxglove aphid); Chaetosiphon fragaefolii Attorney Docket: 36446.0365P1 Cockerell (strawberry aphid); Diuraphis noxia Kurdjumov / Mordvilko (Russian wheat aphid); Dysaphis plantaginea Paaserini (rosy apple aphid); Eriosoma lanigerum Hausmann (woolly apple aphid); Brevicoryne brassicae Linnaeus (cabbage aphid); Hyalopterus pruni Geoffroy (mealy plum aphid); Lipaphis erysimi Kaltenbach (turnip aphid); Metopolophium dirrhodum Walker (cereal aphid); Macrosiphum euphorbiae Thomas (potato aphid); Myzus persicae Sulzer (peach-potato aphid, green peach aphid); Nasonovia ribisnigri Mosley (lettuce aphid); Pemphigus spp. (root aphids and gall aphids); Rhopalosiphum maidis Fitch (corn leaf aphid); R. padi Linnaeus (bird cherry-oat aphid); Schizaphis graminum Rondani (greenbug); Sipha flava Forbes (yellow sugarcane aphid); Sitobion avenae Fabricius (English grain aphid); Therioaphis maculata Buckton (spotted alfalfa aphid); Toxoptera aurantii Boyer de Fonscolombe (black citrus aphid) and T. citricida Kirkaldy (brown citrus aphid); Melanaphis sacchari (sugarcane aphid); Adelges spp. (adelgids); Phylloxera devastatrix Pergande (pecan phylloxera); Bemisia tabaci Gennadius (tobacco whitefly, sweetpotato whitefly); B. argentifolii Bellows & Perring (silverleaf whitefly); Dialeurodes citri Ashmead (citrus whitefly); Trialeurodes abutiloneus (bandedwinged whitefly) and T. vaporariorum Westwood (greenhouse whitefly); Empoasca fabae Harris (potato leafhopper); Laodelphax striatellus Fallen (smaller brown planthopper); Macrolestes quadrilineatus Forbes (aster leafhopper); Nephotettix cinticeps Uhler (green leafhopper); N. nigropictus Stål (rice leafhopper); Nilaparvata lugens Stål (brown planthopper); Peregrinus maidis Ashmead (corn planthopper); Sogatella furcifera Horvath (white-backed planthopper); Sogatodes orizicola Muir (rice delphacid); Typhlocyba pomaria McAtee (white apple leafhopper); Erythroneoura spp. (grape leafhoppers); Magicicada septendecim Linnaeus (periodical cicada); Icerya purchasi Maskell (cottony cushion scale); Quadraspidiotus perniciosus Comstock (San Jose scale); Planococcus citri Risso (citrus mealybug); Pseudococcus spp. (other mealybug complex); Cacopsylla pyricola Foerster (pear psylla); Trioza diospyri Ashmead (persimmon psylla). Agronomically important species of interest from the order Hemiptera include, but are not limited to: Acrosternum hilare Say (green stink bug); Anasa tristis De Geer (squash bug); Blissus leucopterus leucopterus Say (chinch bug); Corythuca gossypii Fabricius (cotton lace bug); Cyrtopeltis modesta Distant (tomato bug); Dysdercus suturellus Herrich-Schäffer (cotton stainer); Euschistus servus Say (brown stink bug); E. variolarius Palisot de Beauvois (one-spotted stink bug); Graptostethus spp. (complex of seed bugs); Leptoglossus corculus Say (leaf-footed pine seed bug); Lygus lineolaris Palisot de Beauvois (tarnished plant bug); L. Hesperus Knight (Western tarnished plant bug); L. pratensis Linnaeus (common meadow Attorney Docket: 36446.0365P1 bug); L. rugulipennis Poppius (European tarnished plant bug); Lygocoris pabulinus Linnaeus (common green capsid); Nezara viridula Linnaeus (southern green stink bug); Oebalus pugnax Fabricius (rice stink bug); Oncopeltus fasciatus Dallas (large milkweed bug); Pseudatomoscelis seriatus Reuter (cotton fleahopper). Furthermore, embodiments may be effective using Hemiptera such as, Calocoris norvegicus Gmelin (strawberry bug); Orthops campestris Linnaeus; Plesiocoris rugicollis Fallen (apple capsid); Cyrtopeltis modestus Distant (tomato bug); Cyrtopeltis notatus Distant (suckfly); Spanagonicus albofasciatus Reuter (whitemarked fleahopper); Diaphnocoris chlorionis Say (honeylocust plant bug); Labopidicola allii Knight (onion plant bug); Pseudatomoscelis seriatus Reuter (cotton fleahopper); Adelphocoris rapidus Say (rapid plant bug); Poecilocapsus lineatus Fabricius (four-lined plant bug); Nysius ericae Schilling (false chinch bug); Nysius raphanus Howard (false chinch bug); Nezara viridula Linnaeus (Southern green stink bug); Eurygaster spp.; Coreidae spp.; Pyrrhocoridae spp.; Tinidae spp.; Blostomatidae spp.; Reduviidae spp. and Cimicidae spp. Also included are adults and larvae of the order Acari (mites) such as Aceria tosichella Keifer (wheat curl mite); Petrobia latens Müller (brown wheat mite); spider mites and red mites in the family Tetranychidae, Panonychus ulmi Koch (European red mite); Tetranychus urticae Koch (two spotted spider mite); (T. mcdanieli McGregor (McDaniel mite); T. cinnabarinus Boisduval (carmine spider mite); T. turkestani Ugarov & Nikolski (strawberry spider mite); flat mites in the family Tenuipalpidae, Brevipalpus lewisi McGregor (citrus flat mite); rust and bud mites in the family Eriophyidae and other foliar feeding mites and mites important in human and animal health, i.e., dust mites in the family Epidermoptidae, follicle mites in the family Demodicidae, grain mites in the family Glycyphagidae, ticks in the order Ixodidae. Ixodes scapularis Say (deer tick); I. holocyclus Neumann (Australian paralysis tick); Dermacentor variabilis Say (American dog tick); Amblyomma americanum Linnaeus (lone star tick) and scab and itch mites in the families Psoroptidae, Pyemotidae and Sarcoptidae. Insect pests of the order Thysanura are of interest, such as Lepisma saccharina Linnaeus (silverfish); Thermobia domestica Packard (firebrat). Additional arthropod insects covered include: spiders in the order Araneae such as Loxosceles reclusa Gertsch and Mulaik (brown recluse spider) and the Latrodectus mactans Fabricius (black widow spider) and centipedes in the order Scutigeromorpha such as Scutigera coleoptrata Linnaeus (house centipede). Attorney Docket: 36446.0365P1 Insects of interest include the superfamily of stink bugs and other related insects including but not limited to species belonging to the family Pentatomidae (Nezara viridula, Halyomorpha halys, Piezodorus guildini, Euschistus servus, Acrosternum hilare, Euschistus heros, Euschistus tristigmus, Acrosternum hilare, Dichelops furcatus, Dichelops melacanthus, and Bagrada hilaris (Bagrada Bug)), the family Plataspidae (Megacopta cribraria - Bean plataspid) and the family Cydnidae (Scaptocoris castanea - Root stink bug) and Lepidoptera species including but not limited to: diamond-back moth, e.g., Helicoverpa zea Boddie; soybean looper, e.g., Pseudoplusia includens Walker and velvet bean caterpillar e.g., Anticarsia gemmatalis Hübner. Nematodes include parasitic nematodes such as root-knot, cyst and lesion nematodes, including Heterodera spp., Meloidogyne spp. and Globodera spp.; particularly members of the cyst nematodes, including, but not limited to, Heterodera glycines (soybean cyst nematode); Heterodera schachtii (beet cyst nematode); Heterodera avenae (cereal cyst nematode) and Globodera rostochiensis and Globodera pailida (potato cyst nematodes). Lesion nematodes include Pratylenchus spp. As used herein, “insects” does not include nematodes. Methods for measuring pesticidal 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 78:290-293 and US Patent Number 5,743,477, all of which are herein incorporated by reference in their entirety. Generally, the protein is mixed and used in feeding assays. See, for example Marrone, et al., (1985) J. of Economic Entomology 78:290-293. Such assays can include contacting a food source with one or more insects and determining the insect’s ability to survive. Systems and methods are provided for sorting insect samples and preparing infestation tests. Generally, insect samples can be provided an unsorted configuration (e.g., unevenly distributed across a plate). The insect samples can be placed onto a lab tray. The lab tray can, for example, include a plurality of wells (e.g., optionally, 24, 48, or 96 wells) that are each configured to receive one or more insect samples therein. It can be advantageous to infest with live insects (e.g., larvae) instead of eggs because a) unknown viability of eggs leads to uncertainty, and b) live larva require less time to complete an experiment. Accordingly, using live insects can decrease the need for redundancy and lower the number of lab trays used, thereby lowering cost of the experiments. Attorney Docket: 36446.0365P1 However, sorting live insects can be particularly difficult for various reasons. For example, live insects are prone to move. Accordingly, it can be difficult to contain the insects on the plate in the unsorted configuration. Further, once placed on the lab tray, insects can move from their desired positions (e.g., within specific wells). Accordingly, it is desirable to complete the sorting of the insects on a lab tray quickly before the insects are able to move to undesired locations. Still further, unlike infestations using eggs, live insects move in the unsorted configuration, which requires techniques such as continuous image processing to identify the current location of the insects for sorting. In further aspects, the disclosed systems and methods can be used for sorting eggs. It is contemplated that infesting with eggs can allow flexibility to work with difficult larva species that have excess frass, silk, or sensitivity to environmental conditions like humidity. The disclosed systems and methods can infest more mature larva and insects for other lab test preparation processes. The disclosed systems and methods can handle multiple experiments simultaneously. The disclosed systems and methods can reduce costs. The disclosed systems and methods can permit scaling for changing (e.g., growing or shrinking) output needs. The disclosed systems and methods permit incorporation of various other functionalities such as, for example, leaf disc transfer. The disclosed systems and methods can reduce or eliminate ergonomic concerns with manual infesting processes. The disclosed system can be used to perform infestation experiments with a plurality of different species (e.g., twelve different species). The disclosed system can be used to infest insects and more mature larva than other infesting methods. Uses of the disclosed systems and methods are not limited to operations such as infestation experiments. For example, the disclosed systems and methods can be used for insect rearing (e.g., for moving an arthropod from one container to another). Other difficulties that the disclosed systems and methods address include the following: ^ Larvae can be delicate. The viability of the larvae after infestation is important to not skew experiment data. If larvae are injured, then they may die prematurely, thereby affecting test results. ^ Larvae can be very small • Conventional suction cups may not be an option • Small holes are advantageous to prevent insects from crawling out of the lab tray Attorney Docket: 36446.0365P1 ^ Larvae are placed into lab trays within hours of hatching. Their objective is to find food before they die. Larvae scatter quickly in a short period of time and need to be contained in any infestation process. • This aspect drives a need to fill and seal the lab tray as quickly as possible • Efforts are needed to keep the system from itself becoming infested ^ Larvae are moisture sensitive and can desiccate in certain environments ^ Some larvae produce excessive silk and frass causing larva to adhere to tools when not desirable ^ Some larvae will chew through thin metal components ^ In some situations Larva can clump or stick together forming balls of larvae that cannot be used to infest. In these aspects, larvae can be divided into smaller groups or singulated ^ There are over a dozen species with similarities and differences. The disclosed system desirably is versatile and capable of handling different species. ^ Insect eggs can stick together Referring to FIGS.1-3, a system 200 can comprise at least one sorting module 1. In various aspects, the system 200 can include a plurality of sorting modules 1. In other aspects, the system can include only one sorting module 1. Each sorting module 1 can be configured to distribute insect samples onto a lab plate, optionally from a tray having insect samples thereon. In some aspects, the system 200 can comprise a plate sealing assembly 3 that is configured to seal the lab plate to contain the insect sample within the lab plate. For example, the plate sealing assembly can apply a film (e.g., a polymer film and, optionally, a polyester film such as Mylar®polyester film) to the lab plate. The system can further comprise a piercing assembly 2 that is configured to pierce the film over the lab plate. The piercing assembly 2 can be configured to pierce the lab plate to form holes that permit air exchange but inhibit movement of insect samples therethrough. For example, in some aspects, the piercing assembly 2 is configured to form holes having a diameter of no greater than 0.50 mm, or no greater than 0.40 mm, or no greater than 0.35 mm, or no greater than 0.30 mm, or no greater than 0.25 mm. The piercing assembly 2 can be configured to interchangeably use different piercing elements that form holes of different sizes. In some optional aspects, the piercer can be configured to simultaneously pierce a plurality of holes in the lab plate. For example, the piercing assembly 2 can simultaneously Attorney Docket: 36446.0365P1 hold a plurality of piercers 290 (FIGS.26B-27). In some aspects, the piercers 290 can be selected to form a predetermined number of holes (e.g., one hole, two holes, three holes, or more) to provide a desired amount of airflow. For example different piercers can have different numbers of needles or different sizes of needles. FIG.26A shows a conventional piercer 290’. Said conventional piercer is designed for completely opening the well of the lab plate so that other processes (e.g., liquid transferring) can reach into the well. FIG.26B further shows a piercer 290 that is configured to be used with a conventional piercing assembly. Piercer 290 can include a body 291 that is receivable into the piercing assembly. The piercer 290 can further include a sleeve that is receivable around the body, with one or more needles 292 secured between the body and the sleeve. Referring also to FIG.27, a piercer 290 can include an outer body 294 that is receivable into the piercing assembly 2. The outer body 294 can define a bore 295. The piercer 290 can further include an inner fastening body 296 that is receivable into the bore 295 of the outer body 294, with one or more needles 292 secured therebetween. The piercer 290 can further include a cover that biases against a top of the needle, and a fastener can secure the cover to the inner fastening body 296. In some aspects, the inner fastening body 296 can define slots that receive portions of the needle(s) 292. The inner fastening body 296 can define feature (e.g., a projection) that cooperates with a corresponding feature (e.g., a groove) to rotationally align the outer body 294 and inner fastening body 296. It is contemplated that the piercer 290 illustrated in FIG.27 can advantageously (as compared to, for example, the conventional piercer of FIG.26B) be dismantled and reassembled to permit easy servicing (e.g., when needles are bent, blunted, damaged, or otherwise need replacement). The system 200 can further comprise a service robot 15 that is configured to move objects within the system. For example, the service robot 15 can transport a tray (e.g., having insect samples thereon) to and from the sorting module 1. In further aspects, the service robot can be configured to transport the lab plate to the plate sealing assembly 3. Sorting Module Referring to FIGS.3-5, the sorting module 1 can comprise a receiving area 210 for receiving a tray 212 having thereon a plurality of insect samples. The sorting module 1 can further comprise a lab plate area 220 for receiving a lab plate 222. In exemplary aspects, the sorting module 1 can use image processing and a robot for picking and placing the insect samples. For example, in some aspects, the sorting module can further comprise a camera 230 configured to capture images of the plurality of insect samples Attorney Docket: 36446.0365P1 on the tray when received within the receiving area and a computing device (e.g., imaging computers 12, 13, shown in FIGS.8A and 9) in communication with the camera 230. In exemplary aspects, the camera 230 can be provided as an imaging assembly 7 (FIG.8A), further disclosed herein. The camera 230 and computing device can cooperate to provide an image processing system 234 that is configured to identify a location of an individual insect sample of the plurality of insect samples on the tray 212. It should be understood that the location of the individual insect sample can refer to a single individual insect sample, or the location of a single individual insect sample that is within a group of insect samples. The sorting module 1 can comprise a robot 240 that is configured to pick up the individual insect sample based on the respective locations of the individual insect sample identified by the image processing system 234 and place the individual insect sample into the lab plate 222 in the lab plate area 220. In exemplary aspects, the robot 240 can be embodied as a robotic arm 10, further disclosed herein. The robot 240 can comprise, for example, a multi-axis robotic arm or a gantry. In some aspects, the computing device of the image processing system 234 can be a dedicated image processing computing device. In other aspects, the computing device of the image processing system 234 can be operative to perform additional functions, including, for example, control of the robot 240. Referring to FIGS.5 and 14-20, in some aspects, the robot 240 can comprise at least one end effector 250 having an outlet 252. The robot 240 can be configured to apply a vacuum at the outlet 252 of the at least one end effector 250 to pick up the individual insect sample. For example, the robot 240 can be in communication with a vacuum pump or other vacuum source. In some aspects, the at least one end effector 250 can comprise a restrictor 254 that is configured to restrict airflow therethrough to reduce the vacuum applied at the outlet 252. For example, the restrictor can comprise a tubing 256 having a select cross sectional area. In other aspects, the restrictor can be defined by a bore through the end effector 250. More generally, the restrictor 254 can be any structure that provides a desired vacuum cross sectional area. As illustrated in FIGS.14 and 18, the at least one end effector can further comprise a screen 258 positioned over the outlet of the at least one end effector. The screen 258 can comprise, for example, sintered metal, wool, or paper. The screen 258 can be a filter that catches the insect sample and prevents the insect sample from moving through the outlet 252. Attorney Docket: 36446.0365P1 In some aspects, the end effector 250 can comprise a recess (e.g., an annular recess) that receives adhesive for adhering the screen across the outlet. In some aspects, the robot 250 can be further configured to apply a positive pressure across the outlet 252 of the at least one end effector 250 to release the individual insect sample. Referring to FIGS.18-20, the at least one end effector can comprise a cover 260 that is configured to cover at least a portion of a top opening of a well of the lab plate 222 (FIG. 5). For example, in some aspects, the cover 260 can be configured to be at least partly received into the well. In other aspects, the cover 260 can be configured to extend across the top opening of the well of the lab plate. In exemplary aspects, the cover 260 can comprise a ring that circumferentially surrounds the outlet 252 of the at least one end effector 250. In various optional aspects, the cover 260 of the at least one end effector can comprise at least one groove 262 (e.g., a plurality of radially extending grooves) that permits exhaust of gas therethrough while inhibiting movement of the individual insect sample therethrough. In this way, as positive pressure is applied across the outlet 252 to release the insect sample, the air can pass by the cover 250 without blowing the insect sample from the well. As illustrated in FIG.20, the cover can comprise a port that permits excess adhesive to flow therethrough, ensuring that adhesive does not interfere with the operation of the end effector. In some aspects, the robot 240 can have only one single end effector 250 (FIG.24). In other aspects, and as illustrated in FIGS.21-23, the robot 240 can comprise a plurality of end effectors provided as an end effector assembly. Optionally, in these aspects, the robot 240 can be configured to vertically articulate the position of each end effector 250 relative to each other end effector of the plurality of end effectors. For example, the robot 240 can lower one end effector relative to the others to pick up and / or release each insect sample. In various aspects, the end effectors can be movably positioned on respective tracks (e.g., via guide rail and carriage). Electric motors or pneumatic actuators can actuate vertical movement of the end effectors 250. For example, as illustrated, each end effector can comprise a respective air cylinder that effects vertical movement of the end effectors. The end effector assembly can comprise an air manifold that is coupled to each end effector with a respective flexible channel tubing. In some aspects, the end effectors can comprise removable tips that permit adaptability of the end effector 250 for different operations. The removable tips can be threaded for selectively coupling the tips to the end effector assembly. It is contemplated that using a plurality of end effectors 252 can reduce infestation time. For example, in some aspects, the plurality of end effectors 252 can be spaced along an Attorney Docket: 36446.0365P1 axis. The spacing of adjacent end effectors can be the same as the spacing of adjacent wells so that the end effectors can simultaneously align with respective wells of the lab plate 222. In some aspects, the sorting module 1 can further comprise a robot cleaning assembly that is configured to clean the at least one end effector. The robot cleaning assembly can comprise, for example, a spray nozzle, a brush, or a combination thereof. The robot cleaning assembly can be positioned within the sorting module so that the robot can move the end effector to the robot cleaning assembly. For example, in some aspects, the robot cleaning assembly can comprise a brush, and the robot can move the end effector across the brush to remove any undesirable material (e.g., particulates, insect samples, etc.). Additionally or alternatively, as further described herein, the end effector can exhaust air through the outlet 252 to remove undesirable material from the end effector. It is contemplated that expulsion of the exhaust air can be automatically programmed into a routine of the robot. In some aspects, the sorting module 1 can comprise a leaf sample excisor that is configured to cut a portion of a leaf. In some aspects, the leaf sample excisor can position the portion of the leaf within a well of the lab plate. In other aspects, the robot 240 can be configured to receive the leaf sample and place the leaf sample in the lab plate. For example, the end effector 250 illustrated on the far left of FIG.16 can have a diameter that is small enough to be received within a well of the lab plate and, therefore, can advantageously be used for placing leaf samples (e.g. leaf discs) within the lab plate. In other aspects, the robot end effector can comprise a suction cup for gripping and transferring leaf samples. Referring to FIGS.2 and 25, in some embodiments, the robot 240 can comprise at least one end effector 250’ having an outlet 252’. The robot 240 can be configured to form, at the outlet of the end effector (e.g., a pipet), a drop of liquid that is sufficient to adhere to the individual insect sample. The drop of liquid can remain attached to the pipet. The robot 240 can further be configured to aspirate the liquid into the outlet 252’ to pull the individual insect sample therein. The robot 240 can further be configured to dispense the liquid with the individual insect sample therein into the lab plate. The robot 240 can further be configured to use compressed air to remove any remaining water on the outlet after the insect is dispensed to ensure that no amount of water or other liquid is present when the process repeats. In some exemplary aspects, the robot 240 can comprise at least one end effector that is configured to selectively apply a static electricity charge to releasably hold the individual insect sample thereto. In some aspects, the robot 240 can comprise at least one end effector, the at least one end effector comprising a brush that is configured to adhere to the individual insect sample. Attorney Docket: 36446.0365P1 The robot can further comprise an air source. A blow-off tube can be configured to receive air from the air source. The blow-off tube can have an outlet that is positioned to blow the individual insect sample from the brush when the air source supplies air to the blow-off tube. In exemplary aspects, the lab plate 220 can comprise a plurality of openings (e.g., recesses) that are configured to receive the predetermined number of insect samples. For example, the lab plate 220 can serve to meter the insect samples (e.g., single insect samples or predetermined multiples insect samples within each opening). The robot can receive the insects from the predetermined plurality of openings. In this way, the number of insect samples picked up at a time can be easily managed. Further, by containing the insect samples in the plurality of openings, movement of the insects can be inhibited, thereby facilitating the process of picking up the insect samples. Insect Containment In various aspects, the sorting module 1 can be configured to sedate the insect samples using, for example, cooling and / or CO2. For example, referring to FIGS.11-12, in some aspects, the sorting module can comprise a cooling system 280 that is configured to cool the plurality of insect samples on the tray received within the receiving area. Optionally, in these aspects, the cooling system 280 can comprise a chiller 282 below the receiving area. The tray 212, or a portion thereof, can have a high thermal conductivity in order to permit the chiller below the receiving area to cool the insect samples. For example, the tray 212 can comprise a base 214 having thermally conductive walls. The tray 212 can further comprise a tray insert 216 and a plurality of stand-offs 218 that space the tray insert 216 from the base 214, thereby providing a gap for thermal insulation. It is contemplated that cooling only a perimeter of the tray and not the area that the insects are on allows the insects to disperse more evenly. When the insects are chilled, the insects often either move very slowly or not at all. Having a warmer interior helps permit the insects to disperse when they start out clumped together or are in such high concentrations that the vacuum tips could have a tendency to pick up more insects than desired. The chilled perimeter of the tray can inhibit or prevent the insects from crawling out of the tray and on to machine components that are permanent to the apparatus. Furthermore, the tray 212 can have attached to it a vibratory component helping to disperse insects that are not evenly spread apart. The surface of the tray 212 that the insects are on could also be patterned, or textured, to provide a predictable insect distribution, or to prevent the congregation of insects in undesirable areas of the tray. Attorney Docket: 36446.0365P1 Referring to FIG.13, the receiving area 210 can comprise an inlet 270 for receiving gasified dry ice. The receiving area 210 can further comprise a gas trough 272 covered by thermally conductive walls 274. The gasified dry ice can flow from the inlet 270, through the gas trough, thereby cooling the thermally conductive walls 274, and out an outlet 276. In some optional aspects, the sorting module 1 (FIG.1) can comprise a CO2 dispenser that is configured to intermittently dispense CO2at the tray when received within the receiving area. It is contemplated that intermittent dispensing can sufficiently sedate the insect samples without harming the insect samples. In various further aspects, the sorting module 1 (FIG.1) can employ various features for containing insect samples on the tray. For example, referring to FIG.10, in some aspects, the sorting module can comprise an air curtain that is configured to contain insect samples on the tray when received within the receiving area. For example, the tray 212 (FIG.5) can be received within an area surrounded by a structure 302 that guides airflow centrally. Said structure 302 can comprise a peripheral pathway 304 and a lip 306 that serves as a baffle to guide airflow from the pathway 304 in a desired air curtain direction (e.g., upwardly, as illustrated). In further aspects, the sorting module can comprise a vibrator that is configured to vibrate the tray sufficiently to inhibit movement of the insect samples. For example, the sorting module can comprise a vibrating wall that inhibits insects from climbing the wall. In still further aspects, the tray can comprise a boundary defined by a color change that is configured to inhibit movement of insect samples thereacross. For example, the tray 212 can include a light-to-dark change that defines such a boundary across which inhibit insect samples are less prone to move. In further aspects, lighting can be used to form boundaries across which insect sample travel is inhibited. For example, lighting can be used to form extreme lighting changes that inhibit travel of insect samples thereacross. In other aspects, an attractive color (e.g., yellow) can be used to inhibit insect samples from moving away from desired locations. In other aspects, insect samples can be placed in shallow water. In still other aspects, insect samples can be placed in a container having one or more small holes that permit the insect samples to move through in single file so that as insect samples sequentially move through the one or more small holes, they can be picked up by the robot for placement in a lab plate. The image processing system 234 can be configured to detect each insect sample as it moves / emerges from the one or more small holes. In further aspects, the tray can comprise a boundary defined by grease that is configured to inhibit movement of insect samples thereacross. In some aspects, the tray can Attorney Docket: 36446.0365P1 comprise a liquid moat that is configured to inhibit movement of insect samples thereacross. The liquid can be or include, for example, water or alcohol. Computation In some aspects, the computing device can be configured to apply blob detection to detect the respective locations of individual insect sample of the plurality of insect samples on the tray. In some aspects, the computing device can be configured to apply a machine learning algorithm to detect the respective locations of individual insect sample of the plurality of insect samples on the tray. The criteria for training the machine learning module can include, for example, one or more of size, shape, number of proximal insects, position on the tray (e.g., to pick the insects closest to the edge before they are lost to condensation on the chilled tray walls), color for insect development purposes and preferences toward insects that are moving at all to ensure that the insect is alive. The image processing system can be configured to provide real-time insect sample location data to accommodate for insect sample movement. It is contemplated that the processing speed can be selected based on insect movement speed and camera resolution. The camera resolution can be selected to provide an adequate image resolution to distinguish individual insects. In some aspects, the image processing system can be configured to determine, in real- time, a number of insect samples remaining on the tray. In some aspects, the computing device of the image processing system 234 (e.g., imaging computers 11, 12) can be configured to coordinate with a programmable logic controller (PLC) of the robot 240 to coordinate movement of the robot and actuation of the end effectors. In other aspects, a single PLC can serve as the imaging computer and control the robot. In these aspects, the sorting module 1 can comprise a PLC that coordinates all steps of picking and placing. The PLC of the robot 240 can control timing of pneumatic and vacuum elements of the robot for picking up and releasing insects. The PLC can further control operation of the camera and associated lighting. In this way, image analysis can provide real-time object coordinates, minimizing delay between image capture and use of image processing to pick up and place insect samples. In some aspects, the camera can be calibrated to the robot in order 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 a captured image of the coordinate system can be processed by an algorithm that determines an origin, an X axis, and a Y axis of the coordinate system. While the coordinate Attorney Docket: 36446.0365P1 system is still attached and not allowed to move, the end effector of the robot, while attached to the robot, can be moved (e.g., manually moved) to the origin, extent of the X axis and then the extent of the Y axis. These points can be entered into the robot program using an integrated teaching software. Thus, when complete, the camera and robot can be taught the same coordinate system and work in conjunction with each other as described herein. Further Exemplary Sorting Modules Referring to FIG.28, a sorting module 1 can comprise a lab plate area 220 for receiving a lab plate and an insect sample dispenser that is configured to dispense insect samples into the lab plate within the lab plate area. The insect sample dispenser can comprise a receiving space that is configured to receive a plurality of insect samples. The insect sample dispenser can further comprise a metering tray having a plurality of holes that are configured to receive respective insect samples of the plurality of insect samples from the receiving space. The plurality of holes are arranged to be positioned above respective wells of the lab plate. For example, the plurality of holes can have spacing that corresponds to spacing of wells of the lab plate. A slide gate can be configured to slide relative to the metering tray to release the insect sample within the plurality of holes into the lab plate within the lab plate area. In various aspects, an upper chamber 310 can contain dry ice or CO2 gas can be used for sedating insect samples. A lower chamber 320 can contain insect samples. A shutter between the upper and lower chambers can control temperature within the lower chamber and / or CO2exposure in the lower chamber. The lower chamber 320 can comprise the metering tray having the plurality of holes. In some aspects, the assembly illustrated in FIG.28 can be attached to an automated device, such as a robot or gantry system. Dry ice can be added to the top chamber 310 where the sublimated CO2gas permeates to a middle chamber 330 through holes. A gate 340 can open (e.g., axially retract from the wall defining the division between the upper and middle chambers when cooler temperatures are needed allowing the CO2gas to enter into the middle chamber. The CO2gas can be allowed to permeate through holes extending between the middle and lower chambers. Insects can be located in the lower chamber where the CO2gas sedates them. A thermistor and / or oxygen sensor can located in the lower chamber to monitor temperature or oxygen levels to control the lower chamber environment as desired for the insect species using the gate 340. A plurality of holes 350 in the bottom of the lower chamber can align Attorney Docket: 36446.0365P1 with the wells of a lab plate that is to receive the insects. The holes 350 can be sized such that a desired number of insect samples (e.g., larva or eggs) fit into the hole. The assembly can be shaken or moved abruptly to allow the collection of sedated insects to fall into the holes (and pass over the holes when the holes are already full of insect samples). The final position of the excess insects at this stage would be off to the sides of the lower chamber. A slide gate 360 under the holes 350 and located up against the bottom outside surface of the lower chamber can prevent insects from falling out of the holes into the lab plate wells. Only when this slide gate is moved to the open position (when holes of the slide plate 360 align with the holes 350 in the bottom of the lower chamber 320) do the array of holes in the slide gate line up with the lower holes of the lower chamber and the wells of the lab plate allowing the measured insects to fall into the lab plate holes. If needed, a slight increase in air pressure in the lower chamber can encourage the measured insects within the holes 350 to be pushed out of the holes that are aligned with the wells of the lab plate. A support plate below the slide gate can be fixed to the lower chamber so that it is not movable relative thereto. The support plate can define an array of holes that align with the wells of the lab plate to permit the insect samples to drop into the wells of the lab plate. The support plate can ensure the absence of a gap between the holes of the lower chamber and the slide gate that would permit insect samples to pass therethrough. The gap between the bottom of the support plate and the top of the lab plate can be small or non-existent to help ensure insects do not blow out of the lab plate wells if compressed air is used to push insects out of the lower chamber holes. In some optional aspects, the sorting module can comprise a robot that moves to fill the plurality of holes of the metering plate and move extra insect samples not in the holes out of the way so that the insect samples are accurately measured. It is contemplated that various further sorting modules can be used with insects that are sedated (e.g., with CO2). For example, a sorting module can comprise a CO2 supply that is configured to sedate a plurality of insects and a feeder that is configured to distribute the sedated insects. Referring to FIGS.29A-30, in some aspects the feeder can comprise (or be) a ladder feeder 400. The ladder feeder can reciprocate risers (see FIGS.29A and 29B) to move a predetermined number (e.g., one, two, three, or more) insect samples at a time to a deliver samples from a supply, across an end shelf, and to a destination (e.g., a lab plate well). In some aspects, the feeder can taper toward the end shelf to reduce a number of insect samples, returning insect samples that fall off into a hopper. Attorney Docket: 36446.0365P1 Referring to FIGS.31 and 32, in some aspects the feeder can comprise (or be) a vibratory conveyor 450. A motor on the vibratory feeder can move two flexible legs attached to a trough. The legs can be shaped so that the flex is along one axis of motion. The trough can be designed and attached to the top of the legs. The angle of the legs and the angle of the trough can be selected for the type of movement the insects undergo within the trough. The trough moves with a high frequency and low displacement to propel insects within a hopper. The hopper can be integrated into a V-channel within which the insects can be arranged in a row. The vibratory feeder can cycled on and off as insects are needed. One or more insects can fall off the end of the V-channel into a targeted lab plate well. When the motor stops vibrating the hopper / trough part, no insects fall off into a well. The frequency, magnitude and duration of the vibration motion determine how many insects are placed into a lab plate well under the end of the V-channel. In some aspects the feeder can comprise a metering device that is configured to separate from the plurality of insects a predetermined number of insect samples. For example, the predetermined number of insect samples can be a single insect sample for each well. In some aspects, the metering device can comprise a rotating wheel. The rotating wheel can define an opening that is configured to receive therein the predetermined number of insect samples (e.g., a single insect sample). In some aspects, the metering device can comprise a tray having a plurality of recesses that are configured to receive the predetermined number of insect samples. The tray can comprise a plurality of recesses that are arranged to overlie respective wells of a lab plate. The sorting module can further comprise a conveyor that is configured to position the tray relative to the lab plate so that the lab plate is positioned over the tray. A rotation device can be configured to rotate an arrangement of the tray and the lab plate so that the predetermined number of insect samples dump from the plurality of recesses of the tray into the respective wells of the lab plate. For example, the rotation device can be configured to simultaneously flip the tray and the lab plate. In some aspects, the rotation device can comprise a clamp that biases against a first side of the tray and an opposite side of the lab plate to clamp the tray and the lab plate together. In other aspects, the rotation device can separately grip each of the tray and the lab plate and rotate the pair. A method of using the sorting module as disclosed herein can comprise distributing at least one insect sample into a lab plate. In some aspects, the at least one insect sample can comprise an egg. In some aspects, the at least one insect sample can comprise a larva. Attorney Docket: 36446.0365P1 In some aspects, the method can comprise tumbling the insect samples with a plurality of granules so that the at least one insect sample adheres to respective granules. The at least one insect sample can be distributed into the lab plate by lifting individual granules of the plurality of granules with the at least one insect sample adhered thereto. In some aspects, a sorting module can comprise robotics on both ends of a tube to control a position of each end of the tube. An inline vacuum generator can cause insects to be picked at one end of the tube and drawn through tube to a well of a lab plate on the other end of the tube. In some aspects, a sorting module can comprise a multichambered tray with CO2 dosing at different intervals. For example, the sorting module can comprise an open, chilled tray as illustrated in FIGS.11-12. Enclosed compartments can be provided over the chilled tray. Individual compartments can either have ambient air or have a controlled amount of CO2 gas pumped in. This allows an individual compartment to be dosed with CO2 for the appropriate time and then have the sedated insects picked out to be placed into a lab plate while the other sealed compartments contain air and have active larva. When the chamber that is being picked from approaches a depleted condition, the next chamber can be dosed with the appropriate amount of CO2to 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 try to escape the input tray. It is picked from until most or all available insects have been picked. In the meantime, when appropriate, the third chamber can be dosed with CO2to prepare this chamber for picking by the time the second chamber is depleted. The CO2 dose can be programmed and administered by the machine. Advantages of this concept include: o A large quantity of insects can be loaded into the machine to reduce operator oversight time (permitting more focus on other tasks in the lab) o A large quantity of insects can be loaded into the machine with low risk of escaping the input tray and infesting the machine (better equipment hygiene and lower yield loss of input insects) o Insects don’t receive a toxic amount of CO2 gas, only the optimized dose for their species. A larger single compartment of insects can take longer to deplete and could otherwise have the last insects becoming active and escaping before the compartment was depleted. A larger dose of CO2 gas could risk asphyxiation of some of the insects which leads to insect yield loss, false data points when dead insects are placed into lab plate wells and wasted reps within an experiment wasting time, experimental materials and lab supplies. o This can be an alternative to several smaller input trays that can be processed in sequence based on input from the machine. The smaller compartments can Attorney Docket: 36446.0365P1 be several single boxes or one box with several individual compartments that can be placed into the machine simultaneously. Exemplary Aspects of Machine Learning As described herein, machine learning can be used for controlling selection of insect samples for placement into respective lab plates. In some aspects, selection can be binary, such as, for example, whether or not an insect sample should be placed into a lab plate (e.g., by evaluating whether the insect sample is alive or dead). In further aspects, selection can include an order, for example, determining which insect samples to be picked up next. The computing device (e.g., imaging computers 12, 13, shown in FIGS.8A and 9) can comprise a machine learning module for processing selection of insect samples for placement into respective lab plates. The criteria for training the machine learning module can include, for example, one or more of size, shape, number of proximal insects, position on the tray (e.g., to pick the insects closest to the edge before they are lost to condensation on the chilled tray walls), color for insect development purposes and preferences toward insects that are moving at all to ensure that the insect is alive. In exemplary aspects, the computing device (e.g., imaging computers 12, 13, shown in FIGS.8A and 9) may use a segmentation model when analyzing an image(s) of a tray with insect samples thereon. The segmentation model may be a result of applying one or more machine learning models and / or algorithms to images associated with a plurality of trays with insect samples thereon. Machine learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning platforms include, but are not limited to, naïve Bayes classifiers, support vector machines, decision trees, neural networks, and the like. For example, the computing device may be used to receive and analyze a plurality of images associated with a tray with insect samples thereon using one or more machine learning models and / or algorithms. Each of the plurality of images associated with the tray with insect samples thereon may include one or more features. The plurality of images may include a first portion (“first insect sample images”) and a second portion (“second insect sample images”). The second insect sample images may include insect sample images that are labeled as either having a particular attribute(s) or as not having the particular attribute(s). The computing device (imaging computers 12, 13) may utilize the one or more machine learning models and / or algorithms to determine which of the one or more features of the Attorney Docket: 36446.0365P1 insect sample images are most closely associated with an image having the particular attribute(s) versus not having the particular attribute(s) (or vice-versa). Using those closely associated 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 an insect sample image as having particular attribute(s) based on analyzing pixels of the insect sample image. As used herein, the term “segmentation” refers to analysis of an insect sample image to determine related areas of the insect sample image. In some cases, segmentation is based on semantic content of the insect sample image. For example, segmentation analysis performed on an insect sample image may indicate a region of the insect sample image depicting a particular attribute(s) of the corresponding insect sample. In some cases, segmentation analysis produces segmentation data. The segmentation data may indicate one or more segmented regions of the analyzed insect sample image. For example, the segmentation data may include a set of labels, such as pairwise labels (e.g., labels having a value indicating “yes” or “no”) indicating whether a given pixel in the insect sample image is part of a region depicting a particular attribute(s) 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 plurality of pixels) depicts a first attribute, a second attribute, a combination of attributes, and so on. The segmentation data may include numerical data, such as data indicating a probability that a given pixel (or plurality of pixels) is a region depicting a particular attribute(s) of the corresponding insect sample image. In some cases, the segmentation data may include additional types of data, such as text, database records, or additional data types, or structures. Still further, the segmentation model can further be characterized to evaluate order of picking. For example, a given pixel (or plurality of pixels) corresponding to an insect sample of an insect sample image having a highest score can be selected for picking by the robot 240. Proximity to an edge of the tray can, for example, contribute to increasing the picking score. Turning now to FIG.33, a system 800 is shown. The system 800 may be configured to use machine learning techniques to train, based on an analysis of one or more training data sets 810A-810B by a training module 820, at least one machine learning-based classifier 830 that is configured to classify pixels in an insect sample image as depicting or not depicting a particular attribute(s) of a corresponding insect sample image. The training data set 810A Attorney Docket: 36446.0365P1 (e.g., a first portion of the plurality of images) may comprise labeled portions of images (e.g., labeled as depicting or not depicting a particular attribute(s) of a corresponding preferred insect sample). The training data set 810B (e.g., a second portion of the plurality of images) may also comprise labeled imaging results (e.g., labeled as depicting or not depicting a particular attribute(s) of a corresponding preferred insect sample). The labels may comprise “preferred insect sample” and “non-preferred insect sample.” The second portion of the plurality of images may be randomly assigned to the training data set 810B or to a testing data set. In some implementations, the assignment of data to a training data set or a testing data set may not be completely random. In this case, one or more criteria may be used during the assignment, such as ensuring that similar numbers of images of insect samples with different labels are in each of the training and testing data sets. In general, any suitable method may be used to assign the data to the training or testing data sets, while ensuring that the distributions of sufficient quality and insufficient quality labels are somewhat similar in the training data set and the testing data set. The training module 820 may train the machine learning-based classifier 830 by extracting a feature set from the first portion of the plurality of images in the training data set 810A according to one or more feature selection techniques. The training module 820 may further define the feature set obtained from the training data set 810A by applying one or more feature selection techniques to the second portion of the plurality of images in the training data set 810B that includes statistically significant features of positive examples (e.g., pixels depicting a particular attribute(s) of a corresponding insect sample) and statistically significant features of negative examples (e.g., pixels not depicting a particular attribute(s) of a corresponding insect sample). The training module 820 may extract a feature set from the training data set 810A and / or the training data set 810B in a variety of ways. The training module 820 may perform feature extraction multiple times, each time using a different feature-extraction technique. In an embodiment, the feature sets generated using the different techniques may each be used to generate different machine learning-based classification models 840. For example, the feature set with the highest quality metrics may be selected for use in training. The training module 820 may use the feature set(s) to build one or more machine learning-based classification Attorney Docket: 36446.0365P1 models 840A-840N that are configured to indicate whether or not new images contain or do not contain pixels depicting a particular attribute(s) of the corresponding insect sample. The training data set 810A and / or the training data set 810B may be analyzed to determine any dependencies, associations, and / or correlations between extracted features and the sufficient quality / insufficient quality labels in the training data set 810A and / or the training data set 810B. The identified correlations may have the form of a list of features that are associated with labels for pixels depicting a particular attribute(s) of a corresponding insect sample and labels for pixels not depicting the particular attribute(s) of the corresponding insect sample. The features may be considered as variables in the machine learning context. The term “feature,” as used herein, may refer to any characteristic of an item of data that may be used to determine whether the item of data falls within one or more specific categories. By way of example, the features described herein may comprise one or more pixel attributes. The one or more pixel attributes may include a level of color saturation, a hue, a contrast level, a relative position, a combination thereof, and / or the like. In other aspects, the one or more pixel attributes can be or include a change relative to another picture to determine, for example, insect movement. A feature selection technique may comprise one or more feature selection rules. The one or more feature selection rules may comprise a pixel attribute and a pixel attribute occurrence rule. The pixel attribute occurrence rule may comprise determining which pixel attributes in the training data set 810A occur over a threshold number of times and identifying those pixel attributes that satisfy the threshold as candidate features. For example, any pixel attributes that appear greater than or equal to 8 times in the training data set 810A may be considered as candidate features. Any pixel attributes appearing less than 8 times may be excluded from consideration as a feature. Any threshold amount may be used as needed. A single feature selection rule may be applied to select features or multiple feature selection rules may be applied to select features. The feature selection rules may be applied in a cascading fashion, with the feature selection rules being applied in a specific order and applied to the results of the previous rule. For example, the pixel attribute occurrence rule may be applied to the training data set 810A to generate a first list of pixel attributes. A final list of candidate features may be analyzed according to additional feature selection techniques to determine one or more candidate groups (e.g., groups of pixel attributes). Any suitable computational technique may be used to identify the candidate feature groups using any Attorney Docket: 36446.0365P1 feature selection technique such as filter, wrapper, and / or embedded methods. One or more candidate feature groups may be selected according to a filter method. Filter methods include, for example, Pearson’s correlation, linear discriminant analysis, analysis of variance (ANOVA), chi-square, combinations thereof, and the like. The selection of features according to filter methods are independent of any machine learning algorithms. Instead, features may be selected on the basis of scores in various statistical tests for their correlation with the outcome variable (e.g., pixels that depict or do not depict a particular attribute(s) of a corresponding insect sample). As another example, one or more candidate feature groups may be selected according to a wrapper method. A wrapper method may be configured to use a subset of features and train a machine learning model using the subset of features. Based on the inferences that drawn from a previous model, features may be added and / or deleted from the subset. Wrapper methods include, for example, forward feature selection, backward feature elimination, recursive feature elimination, combinations thereof, and the like. In an embodiment, forward feature selection may be used to identify one or more candidate feature groups. Forward feature selection is an iterative method that begins with no features in the machine learning model. In each iteration, the feature which best improves the model is added until an addition of a new feature does not improve the performance of the machine learning model. In an embodiment, backward elimination may be used to identify one or more candidate feature groups. Backward elimination is an iterative method that begins with all features in the machine learning model. In each iteration, the least significant feature is removed until no improvement is observed on removal of features. Recursive feature elimination may be used to identify one or more candidate feature groups. Recursive feature elimination is a greedy optimization algorithm which aims to find the best performing feature subset. Recursive feature elimination repeatedly creates models and keeps aside the best or the worst performing feature at each iteration. Recursive feature elimination constructs the next model with the features remaining until all the features are exhausted. Recursive feature elimination then ranks the features based on the order of their elimination. As a further example, one or more candidate feature groups may be selected according to an embedded method. Embedded methods combine the qualities of filter and wrapper methods. Embedded methods include, for example, Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression which implement penalization functions to Attorney Docket: 36446.0365P1 reduce overfitting. For example, LASSO regression performs L1 regularization which adds a penalty equivalent to absolute value of the magnitude of coefficients and ridge regression performs L2 regularization which adds a penalty equivalent to square of the magnitude of coefficients. After the training module 820 has generated a feature set(s), the training module 820 may generate a machine learning-based classification model 840 based on the feature set(s). A machine learning-based classification model may refer to a complex mathematical model for data classification that is generated using machine-learning techniques. In one example, this machine learning-based classifier may include a map of support vectors that represent boundary features. By way of example, boundary features may be selected from, and / or represent the highest-ranked features in, a feature set. The training module 820 may use the feature sets extracted from the training data set 810A and / or the training data set 810B to build a machine learning-based classification model 840A-840N for each classification category (e.g., each attribute of a corresponding insect sample). In some examples, the machine learning-based classification models 840A- 840N may be combined into a single machine learning-based classification model 840. Similarly, the machine learning-based classifier 830 may represent a single classifier containing a single or a plurality of machine learning-based classification models 840 and / or multiple classifiers containing a single or a plurality of machine learning-based classification models 840. The extracted features (e.g., one or more pixel attributes) may be combined in a classification model trained using a machine learning approach such as discriminant analysis; decision tree; a nearest neighbor (NN) algorithm (e.g., k-NN models, replicator NN models, etc.); statistical algorithm (e.g., Bayesian networks, etc.); clustering algorithm (e.g., k-means, mean-shift, etc.); neural networks (e.g., reservoir networks, artificial neural networks, etc.); support vector machines (SVMs); logistic regression algorithms; linear regression algorithms; Markov models or chains; principal component analysis (PCA) (e.g., for linear models); multi-layer perceptron (MLP) ANNs (e.g., for non-linear models); replicating reservoir networks (e.g., for non-linear models, typically for time series); random forest classification; a combination thereof and / or the like. The resulting machine learning-based classifier 830 may comprise a decision rule or a mapping for each candidate pixel attribute to assign a Attorney Docket: 36446.0365P1 pixel(s) to a class (e.g., depicting or not depicting a particular attribute(s) of a corresponding insect sample). The candidate pixel attributes and the machine learning-based classifier 830 may be used to predict a label (e.g., depicting or not depicting a particular attribute(s) of a corresponding insect sample) for imaging results in the testing data set (e.g., in the second portion of the plurality of images). In one example, the prediction for each imaging result in the testing data set includes a confidence level that corresponds to a likelihood or a probability that the corresponding pixel(s) depicts or does not depict a particular attribute(s) of a corresponding insect sample. The confidence level may be a value between zero and one, and it may represent a likelihood that the corresponding pixel(s) belongs to a particular class. In one example, when there are two statuses (e.g., depicting or not depicting a particular attribute(s) of a corresponding insect sample), the confidence level may correspond to a value p, which refers to a likelihood that a particular pixel belongs to the first status (e.g., depicting the particular attribute(s)). In this case, the value 1−p may refer to a likelihood that the particular pixel belongs to the second status (e.g., not depicting the particular attribute(s)). In general, multiple confidence levels may be provided for each pixel and for each candidate pixel attribute when there are more than two statuses. A top performing candidate pixel attribute may be determined by comparing the result obtained for each pixel with the known sufficient quality / insufficient quality status for each corresponding image of insect samples in the testing data set (e.g., by comparing the result obtained for each pixel with the labeled images of insect samples of the second portion of the plurality of images). In general, the top performing candidate pixel attribute for a particular attribute(s) of the corresponding insect sample will have results that closely match the known depicting / not depicting statuses. The top performing pixel attribute may be used to predict the depicting / not depicting of pixels of a new image of insect samples. For example, a new image of insect samples may be determined / received. The new image of insect samples may be provided to the machine learning-based classifier 830 which may, based on the top performing pixel attribute for the particular attribute(s) of the corresponding insect sample, classify the pixels of the new image of insect samples as depicting or not depicting the particular attribute(s). The application may provide an indication of one or more user edits made to any of the attributes indicated by the segmentation mask (or any created or deleted attributes) to the computing device. For example, the user may edit any of the attributes indicated by the Attorney Docket: 36446.0365P1 segmentation mask by dragging some of its points to desired positions via mouse movements in order to optimally delineate depictions of boundaries of the attribute(s). As another example, the user may draw or redraw parts of the segmentation mask via a mouse. Other input devices or methods of obtaining user commands may also be used. The one or more user edits may be used by the machine learning module to optimize the semantic segmentation model. For example, the training module 820 may extract one or more features from output images containing one or more user edits as discussed above. The training module 820 may use the one or more features to retrain the machine learning-based classifier 830 and thereby continually improve results provided by the machine learning-based classifier 830. A method may be used for generating the machine learning-based classifier 830 using the training module 820. The training module 820 can implement supervised, unsupervised, and / or semi-supervised (e.g., reinforcement based) machine learning-based classification models 840. The method illustrated is an example of a supervised learning method; variations of this example of training method are discussed below, however, other training methods can be analogously implemented to train unsupervised and / or semi-supervised machine learning models. The training method may determine (e.g., access, receive, retrieve, etc.) first insect sample images associated with a plurality of insect samples (e.g., first insect samples) and second insect sample images associated with the plurality of insect samples (e.g., second insect samples). The first insect samples and the second insect samples may each contain one or more imaging result datasets associated with insect sample images, and each imaging result dataset may be associated with a particular attribute. Each imaging result dataset may include a labeled list of imaging results. The labels may comprise “attribute pixel” and “non- attribute pixel.” The training method may generate a training data set and a testing data set. The training data set and the testing data set may be generated by randomly assigning labeled imaging results from the second insect sample images to either the training data set or the testing data set. In some implementations, the assignment of labeled imaging results as training or test samples may not be completely random. In an embodiment, only the labeled imaging results for a specific insect type and / or class may be used to generate the training data set and the testing data set. In an embodiment, a majority of the labeled imaging results Attorney Docket: 36446.0365P1 for the specific insect type and / or class may be used to generate the training data set. For example, 75% of the labeled imaging results for the specific insect type and / or class may be used to generate the training data set and 25% may be used to generate the testing data set. The training method may determine (e.g., extract, select, etc.) one or more features that can be used by, for example, a classifier to differentiate among different classifications (e.g., “attribute pixel” and “non-attribute pixel.”). The one or more features may comprise a set of imaging result attributes. In an embodiment, the training method may determine a set features from the first insect sample images. In another embodiment, the training method may determine a set of features from the second insect sample images. In a further embodiment, a set of features may be determined from labeled imaging results from an insect type and / or class different than the insect type and / or class associated with the labeled imaging results of the training data set and the testing data set. In other words, labeled imaging results from the different insect type and / or class may be used for feature determination, rather than for training a machine learning model. The training data set may be used in conjunction with the labeled imaging results from the different insect type and / or class to determine the one or more features. The labeled imaging results from the different insect type and / or class may be used to determine an initial set of features, which may be further reduced using the training data set. The training method may train one or more machine learning models using the one or more features. In one embodiment, the machine learning models may be trained using supervised learning. In another embodiment, other machine learning techniques may be employed, including unsupervised learning and semi-supervised. The machine learning models trained may be selected based on different criteria depending on the problem to be solved and / or data available in the training data set. For example, machine learning classifiers can suffer from different degrees of bias. Accordingly, more than one machine learning model can be trained, and then optimized, improved, and cross-validated. 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 may be evaluated using the testing data set. The predictive model may analyze the testing data set and generate classification values and / or predicted values. Classification and / or prediction values may be evaluated to determine whether such values have achieved a desired accuracy level. Attorney Docket: 36446.0365P1 Performance of the predictive model described herein may be evaluated in a number of ways based on a number of true positives, false positives, true negatives, and / or false negatives classifications of pixels in images of insect samples. For example, the false positives of the predictive model may refer to a number of times the predictive model incorrectly classified a pixel(s) as depicting a particular attribute that in reality did not depict the particular attribute. Conversely, the false negatives of the machine learning model(s) may refer to a number of times the predictive model classified one or more pixels of an image of an insect sample as not depicting a particular attribute when, in fact, the one or more pixels do depicting the particular attribute. True negatives and true positives may refer to a number of times the predictive model correctly classified one or more pixels of an image of an insect sample as having sufficient depicting a particular attribute or not depicting the particular attribute. Related to these measurements are the concepts of recall and precision. Generally, recall refers to a ratio of true positives to a sum of true positives and false negatives, which quantifies a sensitivity of the predictive model. Similarly, precision refers to a ratio of true positives a sum of true and false positives. The sorting module 1 can comprise a validation imaging system that images the completed lab plate before being removed. A camera fitted with either a standard or telecentric lens would collect the image and send it to the computing device for analysis of the completed lab plate image. An algorithm or machine learning algorithm to analyze the image can determine whether each individual well within the lab plate contains the desired insects and relay that information to the robot controller to attempt to fill them again. The sorting module 1 can be configured to analyze the content of each individual well within the lab plate and determine if any are deficient in quantity. This data can be relayed to the sorting module robot to attempt to fill the deficient wells with the desired amount of insects any number of times desired. Exemplary Aspects of Disclosed Systems and Methods In some embodiments, methods are provided for sorting insects having a high hatch rate comprising rinsing insects with a rinse solution; discarding floating insects from the rinse solution; sterilizing the insects; separating immature insects from mature insects; and sorting the mature insects using a sorting module. In another embodiment, a method is provided for sorting insects having a high hatch rate comprising removing insect clumps; incubating the Attorney Docket: 36446.0365P1 insects until the insects show signs of development; and sorting the mature insects using a sorting module. In another embodiment, methods are provided for assaying insects. In some embodiments, methods are provided for assaying insects comprising placing an insect sample (e.g., larva or egg) into a well of an assay plate; capturing an image of the well of the assay plate; and determining a metric measurement of the insect in the well of the assay plate. In one embodiment, the metric measurement of the insect comprises the use of the pixel count of the image. Another embodiment relates to a method for preparing insects for a bioassay. In some embodiments, methods are provided for preparing insects for bioassay comprising preparing individually dispersed insects; and dispensing a pre-determined number of individually dispersed insects into each well of an assay plate. Methods are provided for assaying the activity of insecticidal compounds using an automated system. In some embodiments, methods are provided relating to assaying the activity of insecticidal compounds using an automated system comprising providing a multi- well plate with at least one insect sample (e.g., larva or egg) in a predetermined number of wells of a multi-well plate; transporting by automated means the multi-well plate to an incubation device for incubation of the multi-well plate; and transporting by automated means the multi-well plate to a measuring device for measuring movement or determining a metric measurement. Methods are provided for sorting insect eggs. In some embodiments, methods are provided for sorting insect eggs having a high hatch rate comprising rinsing insect eggs with a rinse solution; discarding floating insect eggs from the rinse solution; sterilizing the insect eggs; separating immature insect eggs from mature insect eggs; and sorting the mature insect eggs using a sorting module. In another embodiment, a method is provided for sorting insect eggs having a high hatch rate comprising removing insect eggs clumps; incubating the insect eggs until the insect eggs show signs of development; and sorting the mature insect eggs using sorting module. Another embodiment relates to a method for preparing insect eggs for a bioassay. In some embodiments, methods are provided for preparing insect eggs for bioassay comprising preparing individually dispersed insect eggs; selecting insect eggs which are ready to hatch within a predetermined time frame; and dispensing a pre-determined number of individually dispersed insect eggs into each well of an assay plate. Attorney Docket: 36446.0365P1 In one embodiment, the method of sorting insects comprises sorting insect eggs. In another embodiment, the method of sorting insects comprises sorting insect larvae. In a further embodiment, the method of sorting insects relates to a method of sorting insect eggs and larvae. In one embodiment, a method relating to sorting insects comprises selecting a mature insect egg. A mature insect egg is an insect egg that has a high probability of hatching within a predetermined time frame. In one embodiment, selecting a mature insect egg comprises pretreating an insect. In a further embodiment, the insect egg is sorted by density gradient sorting and / or by a sorting module 1 (or a plurality of sorting modules) as further disclosed herein. In one embodiment, the sorting module 1 may be capable of sorting using fluorescence, size, optical density, and side scatter parameters. In exemplary embodiments, and with reference to FIGS.7-9, the sorting module 1 can comprise a large particle sorting module (e.g., a large particle flow cytometer) as is known in the art. In use, such large particle sorting modules can provide for automated analysis and sorting of insect eggs and insects as further disclosed herein. For example, the large particle sorting module can be capable of sorting objects by length, optical density, and fluorescence. In exemplary configurations, the large particle sorting module can comprise a reservoir pressurized to produce a constant flow rate of fluid through a flow channel. The sample can be contained within a continuously mixing sample container and be sufficiently pressurized to penetrate a laminar sheath stream, resulting in a core sample stream carried by the surrounding reservoir flow and centered in the flow stream where it can be illuminated by at least one visible laser (optionally, a plurality of lasers). The large particle sorting module can further comprise sensors or detectors that measure various parameters, such as time of flight (length of signal), optical density, and fluorescence emissions, which can be analyzed as optical characteristics that can be used as sort criteria. As fluid exits the flow channel, it can be diverted by an air stream to a recovery container. Alternatively, during a sorting operation, the air stream will be turned off (to prevent diverting of the fluid), and droplets of the fluid containing the sortable object can be dispensed by a nozzle. In further embodiments, the sorting module can comprise a stage that is configured to support a multi-well plate that receives droplets from the dispensing nozzle. Objects that were diverted into the recovery container can be retrieved as desired for further assessment and analysis. As further disclosed herein, the sorting module can further comprise a computer 120 that is configured to control operation of the sorting module and that can be loaded with software for conducting data processing and Attorney Docket: 36446.0365P1 sorting operations. In use, the large particle sorting module can be coupled to a large particle sampling assembly, as is known in the art, which can present samples to the large particle sorting module. One example of a large particle sorting module that is suitable for use as a sorting module as disclosed herein is a 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 combination of pre-optimized ranges of green fluorescence, red fluorescence and / or side-scatter readings. In a specific embodiment, the rinse solution comprises a bleach solution, an acidic, and / or an alcohol solution. In one embodiment the rinse solution is a peracetic acid solution. In one embodiment, the rinse solution is an ethanol solution. In another embodiment, a rinsing may occur sequentially, with each rinse comprising a different rinse solution or the same rinse solution. In another embodiment, the method further includes the step of infesting insects into an assay plate. In another embodiment, the insects are sorted prior to infesting an assay plate. In a further embodiment, the insects are infested using the sorting module 1 to infest the assay plates. In this embodiment, the sorting module 1 can be a large particle sorting module that ejects insects into corresponding wells of the assay plates. An assay plate comprises 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, an assay plate may be a microtiter plate. In one embodiment, the method relates to infesting one insect per well. In another embodiment, the method relates to infesting more than one insect per well. In one embodiment, the method relates to infesting a predetermined number of insects equally into a plurality of wells. In another embodiment, the well contains a food source. In one embodiment, the assay plates contain artificial diet food source. In a further embodiment, the artificial diet food source is dyed to prevent or adjust emitted fluorescence from a well in an assay plate. In one embodiment, the assay plates contain live plant material. In a further embodiment, a well contains an insecticidal source. In one embodiment the insecticidal source comprises at least one of the group consisting of an insecticidal protein, an insecticidal silencing element or double stranded RNA, or an insecticidal chemistry. In one embodiment, the method comprises the automated infesting of insects into an assay plate. In another embodiment, the automated infesting comprises the use of a robotic arm to move the assay plates into the proper position for infesting, drying, sealing the assay plates, or punching holes in the sealed assay plates. In this embodiment, the robotic arm 10 can be a component of a larger robot assembly, which can comprise processing circuitry Attorney Docket: 36446.0365P1 positioned in communication with other system components as further disclosed herein. It is contemplated that the robotic arm 10 can comprise an end effector that is configured to selectively engage, orient, position, and disengage an assay plate as further disclosed herein. Optionally, the end effector can comprise a gripper, such as, for example and without limitation, an impactive gripper (e.g., at least one claw or jaw), an astrictive gripper (e.g., a suction apparatus), a contigutive gripper (e.g., a gripper having a surface that comprises glue or is capable of applying surface tension or freezing action), or combinations thereof, It is further contemplated that the robotic arm 10 can comprise a plurality of links that are coupled together at joints that allow for rotational motion or axial translation of links relative to one another. Optionally, it is contemplated that the robotic arm 10 can be a multi-axis robotic arm having multiple degrees of freedom. For example, it is contemplated that the multi-axis robotic arm can be configured for axial movement in a plurality of axes and rotational movement in at least one axis (optionally, a plurality of axes). In one embodiment, the assay plates comprise a barcode. In this embodiment, it is contemplated that the system can further comprise at least one barcode reader as is known in the art, which can be communicatively coupled to a computer or other processing equipment as further disclosed herein. In one embodiment the assay plate is a white, clear, opaque, black or other colored assay plate. In another embodiment, a method is provided for assaying insects comprising placing an insect into a well of an assay plate; capturing an image of the well of the assay plate; and determining a measurement of the insect in the well of the assay plate. In this embodiment, the placement of an insect into a well of an assay plate can be performed using a sorting module as further disclosed herein. It is further contemplated that the image of the well of the assay plate can be recorded using an imaging system, which can comprise at least one imaging assembly 7. Optionally, the imaging system can comprise a camera, a microscope, x-ray equipment, magnetic resonance imaging (MRI) equipment, laser three-dimensional ("3- D") scanners, and various other equipment configured to produce images and identify shapes, patterns, orientation, colors, and or other characteristics of objects. In one embodiment, the measurement of the insect comprises the use of the pixel count of the image recorded by the imaging system. In another embodiment, the measurement comprises the fluorescence of an insect in a well of an assay plate. In one embodiment, the measurement comprises detecting and / or recording the movement of an insect in a well of an assay plate. Optionally, in this embodiment, the movement of the insect can be detected by a machine vision device. Machine vision, as used herein, refers to apparatuses and methods which use electronic Attorney Docket: 36446.0365P1 sensory equipment to electronically identify shapes, colors, patterns, orientation, and / or other characteristics of objects. In this regard, the machine vision device will generally be described herein as being camera-based for purposes of brevity. However, the machine vision device may in some embodiments comprise x-ray equipment, magnetic resonance imaging (MRI) equipment; laser three-dimensional ("3-D") scanners, and various other equipment configured to identify shapes, patterns, orientation, colors, and or other characteristics of objects. Accordingly, the machine vision device, either alone or in combination with processing equipment described herein, may be used to detect movement of an insect as disclosed herein. Optionally, the movement of the insect can be detected using the imaging system (e.g., at least one imaging assembly 7) as disclosed herein. Thus, it is contemplated that the imaging system can comprise at least one camera that is capable of recording an image and, using processing circuitry as disclosed herein, detecting movement as disclosed herein. Additionally, or alternatively, the movement of an insect can be detected and / or recorded using a motion sensor or detector as is known in the art. In a further embodiment, the detecting and / or recording the movement comprises aligning two or more images from a time interval of an insect in a well of an assay plate. In this embodiment, the two or more images can be aligned using imaging software stored on an imaging computer 130, 132 as further disclosed herein. For example, such imaging software can be configured to produce an output corresponding to a visual overlay of discrete images, taken at various times during the time interval, with such output being presented on a display device positioned in communication with a processor of the imaging computer. In use, it is contemplated that the displayed output can create a reference value that can be used to measure changes in movement (or area) over time. In another embodiment, the measurement comprises a metric measurement. In this embodiment, it is contemplated that the metric measurement can be determined using imaging software stored on a computer as further disclosed herein, with the imaging software being configured to determine the size (e.g., body area) of an insect or a portion of an insect by processing a previously captured image of the insect. In exemplary embodiments, the body area measurement can be recorded in combination with other metrics (e.g., length, width, position, light intensity, size differential between time intervals). Additionally, or alternatively, the metric measurement can be determined using a non-contact sensor that is capable of measuring size parameters (e.g., area, distance, length, etc.). Examples of such sensors include optical or laser sensors, gauges, or encoders as are known in the art. However, it is contemplated that any known non-contact measurement sensor can be used. In use, area and position measurements can be Attorney Docket: 36446.0365P1 used to generate insect response statistics, with the other disclosed measurements being used to evaluate and determine insect response. In one embodiment, a method comprising placing an insect into a well of an assay plate; capturing an image of the well of the assay plate; and determining a measurement of the insect in the well of the assay plate is repeated for each well in an assay plate. In another embodiment, a method relates to a bioassay comprising sorting an insect and determining an IC-50, EC-50 or an LC-50. In a further embodiment, a method relates to a bioassay comprising sorting an insect (e.g., using a sorting module as disclosed herein), capturing an image of an insect (e.g., using a camera of an imaging system as disclosed herein), and determining an IC-50, EC-50 or an LC-50. In one embodiment, an IC-50, or EC-50 is determined by a size metric measurement of an insect, which can be performed using imaging software or a sensor as disclosed herein. In one embodiment, an LC-50 is determined by a measurement of movement of an insect. In one embodiment, the method relates to 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 new variant, a shuffled variant, or a domain swapped insecticidal protein. In another embodiment, the test protein is an unknown protein or a protein of unknown toxicity or insecticidal activity to insects. In a further embodiment, the assay comprises the use of a positive control insecticidal protein, wherein the toxicity of the positive control insecticidal protein is known. In one embodiment, the toxicity of a test protein is determined by determining an IC-50, EC-50 or an LC-50 of the test protein. In one embodiment, and with reference to FIGS.7-9, an automated insect bioassay system can comprise at least one sorting module 1 (optionally, a plurality of sorting modules, such as first, second, and third sorting modules 1, 21, 22) as further disclosed herein. Optionally, the bioassay system can further comprise at least one robotic arm 10 (optionally, a plurality of robotic arms 15, 16) as further disclosed herein. Each robotic arm 10, 15, 16 can have its own processing circuitry that can be configured to control operation of the robotic arm and other system components as disclosed herein. Optionally, the processing circuitry of the robotic arm can comprise a central control (master) computer 11, 101 as disclosed herein. Each sorting module 1, 21, 22 can have its own processing circuitry (e.g., a computer 120, 122, 124 as disclosed herein) that is configured to permit selective control of the operation of the sorting module. In use, the processing circuitry of each sorting module can Attorney Docket: 36446.0365P1 be communicatively coupled (e.g., integrally connected or wirelessly connected) to processing circuitry of a robotic arm using conventional mechanisms, such as ActiveX control and / or serial port connection. In another embodiment, the bioassay system can further comprise a plate sealing assembly 3, such as, for example and without limitation, a PlateLoc Thermal Microplate Sealer manufactured by Agilent Technologies, Inc. In one embodiment, as is known in the art, the plate sealing assembly 3 can comprise a stage for receiving an assay plate, a support for a roll of sealing material, a dispensing apparatus for advancing and applying sealing material in a selected amount and at a selected rate, and a user interface that permits control of sealing parameters (e.g., temperature, seal time, etc.). In use, and as further described herein, it is contemplated that the plate sealing assembly 3 can comprise processing circuitry that is communicatively coupled (e.g., integrally connected or wirelessly connected) to processing circuitry of a robotic arm using conventional mechanisms, such as ActiveX control and / or serial port connection. In another embodiment, the automated insect bioassay system can further comprise a piercing assembly 2, such as, for example and without limitation, a Microplate Seal Piercer manufactured by Agilent Technologies, Inc. As is known in the art, the piercing assembly 2 can comprise a stage for receiving an assay plate, a piercing head that is configured for cyclical movement to pierce particular (e.g., selected or predetermined) portions of a seal that has been previously applied over an assay plate, at least one actuator for effecting cyclical movement of the piercing head, and a user interface that is coupled to the actuator and that permits control of the piercing operation. Optionally, the piercing head can define a plurality of projections that are generally vertically aligned with respective wells of the assay plate during a piercing operation. In use, and as further disclosed herein, it is contemplated that the piercing assembly 2 can comprise processing circuitry that is communicatively coupled (e.g., integrally connected or wirelessly connected) to processing circuitry of a robotic arm using conventional mechanisms, such as ActiveX control and / or serial port connection. In another embodiment, the automated insect bioassay system can further comprise at least one imaging assembly 7 (optionally, first and second imaging assemblies 7, 14) as disclosed herein. In this embodiment, the imaging assembly 7 can comprise a camera, a microscope, a sensor, or combinations thereof. The imaging assembly 7 can comprise a stage configured to receive and support an assay plate while a camera or microscope captures images of one or more wells of the assay plate. The imaging assembly 7 can be communicatively coupled to processing circuitry, which can permit selective control of the Attorney Docket: 36446.0365P1 operation (e.g., activation and image acquisition parameters) of the imaging assembly. In use, the processing circuitry of the imaging assembly 7 can be communicatively coupled (e.g., integrally connected or wirelessly connected) to processing circuitry of a robotic arm using conventional mechanisms, such as ActiveX control and / or serial port connection. In another embodiment, the automated insect bioassay system can comprise an evaporator 4, such as, for example and without limitation, an ULTRAVAP microplate evaporator manufactured by Porvair Sciences. As is known in the art, the evaporator 4 can comprise a stage that is configured to receive and support an assay plate, blow-down equipment (e.g., needles, pumps, nozzles, and the like) that is positioned above the stage and capable of accomplishing evaporation of liquid within respective wells of an assay plate, and processing circuitry that permits selective control of the operation (e.g., activation and drying parameters) of the evaporator 4. In use, the processing circuitry of the evaporator 4 can be communicatively coupled (e.g., integrally connected or wirelessly connected) to processing circuitry of a robotic arm using conventional mechanisms, such as ActiveX control and / or serial port connection. In a further embodiment, the automated insect bioassay system can comprise at least one incubator 5 (optionally, a plurality of incubators, such as first and second incubators 5, 6), which can have a housing configured to receive at least one assay plate and equipment (e.g., heating sources, cooling sources, filters, fans, ventilation systems, sensors, and the like) that is configured to control various conditions within the housing (e.g., temperature, humidity, carbon dioxide content, oxygen content, and the like). In this embodiment, each incubator can further comprise processing circuitry that is communicatively coupled to the equipment that controls the various conditions within the housing. In use, the processing circuitry of each incubator 5, 6 can be communicatively coupled (e.g., integrally connected or wirelessly connected) to processing circuitry of a robotic arm using conventional mechanisms, such as ActiveX control and / or serial port connection. In another embodiment, the automated insect bioassay system can comprise at least one plate storage assembly 8, such as, for example and without limitation, a Labware MiniHub manufactured by Agilent Technologies, Inc. In this embodiment, the plate storage assembly can comprise at least one support shaft and a plurality of receptacles mounted along the length of the support shaft for receiving and supporting respective assay plates (or other laboratory equipment). The plate storage assembly 8 can further comprise a rotational actuator configured to effect rotation of the plate storage assembly 8 and thereby provide access to selected assay plates that are stored on the plate storage assembly. The plate Attorney Docket: 36446.0365P1 storage assembly 8 can further comprise processing circuitry that is communicatively coupled to the actuator to permit selective control of the rotational position of the plate storage assembly. Optionally, the automated insect bioassay system can comprise a plurality of plate storage assemblies, such as first and second storage assemblies 8, 17. In use, the processing circuitry of each plate storage assembly 8 can be communicatively coupled (e.g., integrally connected or wirelessly connected) to processing circuitry of a robotic arm using conventional mechanisms, such as ActiveX control and / or serial port connection. It is contemplated that plate storage in the first and second storage assemblies 8, 17 can be static position (e.g., not wired or wirelessly connected). In these aspects, positions can be programmed as available in scheduling software of the system. In a further embodiment, the automated insect bioassay system can comprise at least one plate stacking assembly 9 (optionally, first and second plate stacking assemblies 23, 24), such as, for example and without limitation, a Labware Stacker manufactured by Agilent Technologies, Inc. In this embodiment, plate stacking assembly 9 can comprise a vertical receptacle that contains a plurality of racks that permit stacking and sequential dispensing of assay trays. Optionally, the plate stacking assembly 9 can further comprise an engagement apparatus (e.g., a gripper, a claw) that is configured to sequentially dispense individual assay trays from a stack of assay trays. The plate stacking assembly 9 can further comprise an unloading opening positioned in communication with the vertical receptacle to permit engagement between the robotic arm and a plate that is dispensed by the plate stacking assembly. In use, processing circuitry of the plate stacking assembly 9, which can be configured to permit selective control of the dispensing of plates, can be communicatively coupled (e.g., integrally connected or wirelessly connected) to processing circuitry of a robotic arm using conventional mechanisms, such as ActiveX control and / or serial port connection. In a further embodiment, the automated insect bioassay system can comprise at least one barcode reader (e.g., first and second barcode readers 18, 19) that is positioned at selected locations within the system to permit tracking of the locations of individual plates (which have been previously barcoded as disclosed herein). Each barcode reader can comprise processing circuitry that is configured to transmit information concerning the detection and scanning of barcodes (e.g., time, location, plate identification and the like). Optionally, each barcode reader can be communicatively coupled to a robotic arm. Additionally, or alternatively, each barcode reader can be communicatively coupled to a Attorney Docket: 36446.0365P1 master computer 11, 101 or remote computing device 114a, 114b, 114c as further disclosed herein. As can be appreciated with reference to the examples depicted in FIGS.7-9, it is possible to construct an automated insect bioassay system using components that are fully compatible with robot-implemented systems, with the processing circuitry of the robotic arm 10 (e.g., a control (master) computer 11, 101) communicating with all system components and serving as the central processing unit for the overall system. In this example, it is contemplated that the robotic arm 10 can be positioned centrally within the system, with the remaining system components positioned in a desired arrangement around the robotic arm. In one embodiment, the stages and other accessible areas of the system components can be positioned at an optimal radial position relative to the robotic arm to ensure that those areas of the system components can be easily accessed by the robotic arm when the robotic arm is radially deployed. It is contemplated that such configurations can permit fully automated system operation. In the description of sorting operations provided herein, it is contemplated that all steps of the sorting process can be performed in an automated manner. Where specific structure for performing a step is not provided in the description, it is understood that such step can be performed by corresponding processing circuitry as disclosed herein, which can control operation of system components or conduct analysis in an automated manner. In alternative embodiments, the methods and systems disclosed herein, in whole or in part, necessarily require implementation using a machine, computer system or equivalent, within which a set of instructions for causing the computer or machine to perform any one or more of the protocols or methodologies of the invention may be executed. In alternative embodiments, the machine may be connected (e.g., networked) to other machines, e.g., in a Local Area Network (LAN), an intranet, an extranet, or the Internet, or any equivalents thereof. The machine may operate in the capacity of a server or a client machine in a client- server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. The term "machine" shall also be taken to include any collection of machines, computers or products of manufacture that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies of the invention. Attorney Docket: 36446.0365P1 In alternative embodiments, and with reference to FIG.9, an exemplary computer system of the invention comprises a data network 140 and a communication network 150. In exemplary embodiments, the data network 140 can comprise a control (master) computer 101 and at least one imaging computer (e.g., first and second imaging computers 12, 13), which can be communicatively coupled to the control computer. The communication network 150 can comprise computers 120, 122, 124 of the sorting modules 1, 21, 22 disclosed herein. In one embodiment, the sorting computers 120, 122, 124 can be communicatively coupled to the control computer 101 to communicate data obtained from the sorting modules and to permit control of the sorting module via instructions received from the control computer. The control computer 101 can operate in a networked environment using logical connections to one or more remote computing devices 114a,b,c. By way of example, a remote computing device can be a personal computer, portable computer, smartphone, a server, a router, a network computer, a peer device or other common network node, and so on. Logical connections between the control computer 101 and a remote computing device 114a,b,c can be made via a network 115, such as a local area network (LAN) and / or a general wide area network (WAN). Such network connections can be through a network adapter, which can be implemented in both wired and wireless environments. Optionally, as depicted in FIGS.8A-8B, the control computer 101 can function as a master computer 11, which can provide the processing circuitry for the robotic arm and be communicatively coupled to a robotic arm and other system components as further disclosed herein. The first and second imaging computers 12, 13 can also be connected to remote computing devices through the network 115. FIGS.8A-8B show a non-limiting example of such a configuration, in which the automated insect bioassay system includes a master computer 11 and first and second imaging computers 12, 13. The example illustrates a sorting module 1, a piercing 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 master computer 11, a first imaging computer 12 (communicatively coupled to the first imaging assembly 7), a second imaging computer 13, a second imaging assembly 14 (communicatively 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. Attorney Docket: 36446.0365P1 In one exemplary configuration, the communication network 150 can be a LAN network that handles all scheduling instructions from 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 can be dedicated to image acquisition and transfer of large amounts of image data to the network 115, and the remote computing devices 114a, 114b, 114c can download the image data and perform a series of image processing and statistics analysis. The communication network 150 can be self-contained, able to operate independently from the data network 140. Optionally, it is contemplated that each computer disclosed herein can comprise its own processing device (processor), a system memory, which includes a 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 a static memory (e.g., flash memory, static random access memory (SRAM), etc.), and a data (mass) storage device, which communicate with each other via a bus. The system memory typically contains data such as control processing data and / or program modules such as an operating system and control processing software that are immediately accessible to and / or are presently operated on by the processing unit. Optionally, any number of program modules can be stored on the mass storage device, including by way of example, an operating system and control processing software. Each of the operating system and control processing software (or some combination thereof) can comprise elements of the programming and the control processing software. Control processing data can also be stored on the mass storage device. Control processing data can be stored in any of one or more databases known in the art. Examples of such databases comprise, DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL, PostgreSQL, and the like. The databases can be centralized or distributed across multiple systems. In alternative embodiments, a processor of a computer as disclosed herein represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processor may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processor may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a Attorney Docket: 36446.0365P1 digital signal processor (DSP), network processor, or the like. In alternative embodiments the processor is configured to execute the instructions (e.g., processing logic) for performing the operations and steps discussed herein. In alternative embodiments the computer further comprises a network interface device (adapter). The computer also may include a display device, which can be a video display unit (display device, e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer also may include a human-machine interface, which can include, for example, an alphanumeric input device (e.g., a keyboard), a cursor control device (e.g., a mouse), and a signal generation device (e.g., a speaker). In addition to the human-machine interface, the computer may also include an input / output interface. In alternative embodiments, the data storage device (e.g., drive unit) comprises a computer-readable storage medium on which is stored one or more sets of instructions (e.g., software) embodying any one or more of the protocols, methodologies or functions of this invention. The instructions may also reside, completely or at least partially, within the main memory and / or within the processor during execution thereof by the computer, the main memory and the processor also constituting machine-accessible storage media. The instructions may further be transmitted or received over a network via the network interface device. In alternative embodiments the computer-readable storage medium is used to store data structure sets that define user identifying states and user preferences that define user profiles. Data structure sets and user profiles may also be stored in other sections of computer system, such as static memory. In alternative embodiments, while the computer-readable storage medium in an exemplary embodiment is a single medium, the term "machine-accessible storage medium" can be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store the one or more sets of instructions. In alternative embodiments the term "machine-accessible storage medium" can also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention. In alternative embodiments the term "machine- accessible storage medium" shall accordingly be taken to include, but not be limited to, solid- state memories, and optical and magnetic media. In alternative embodiments, information and signals are represented using any technology and / or technique known in the art. For example, data, instructions, commands, Attorney Docket: 36446.0365P1 information, signals, bits, symbols, and chips used to practice the compositions (devices, computers) and methods of the invention can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. In alternative embodiments, the various illustrative logical blocks, modules, circuits, and algorithm steps used to describe exemplary embodiments of the invention can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the method steps. In addition, the present disclosure is not described with reference to any particular programming language. In alternative embodiments, a variety of programming languages are used to implement the embodiments of the invention as described herein.

[0002] Attorney Docket: 36446.0365P1 EXEMPLARY ASPECTS In view of the described products, systems, and methods and variations thereof, herein below are described certain more particularly described aspects of the invention. These particularly recited aspects should not however be interpreted to have any limiting effect on any different claims containing different or more general teachings described herein, or that the “particular” aspects are somehow limited in some way other than the inherent meanings of the language literally used therein. Aspect 1:A module comprising: a receiving area for receiving a tray having thereon a plurality of insect samples; an imaging processing system configured to identify a location of an individual insect sample of 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 when received within the receiving area; and a computing device in communication with the camera; a lab plate area for receiving a lab plate; and a robot that is configured to: pick up the 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 lab plate in the lab plate area. Aspect 2: The module of aspect 1, further comprising a cooling system that is configured to cool the plurality of insect samples on the tray received within the receiving area. Aspect 3: The module of aspect 2, wherein the cooling system comprises a chiller below the receiving area. Aspect 4: The module of aspect 3, further comprising the tray received within the receiving area, wherein at least a portion of the tray has a high thermal conductivity. Aspect 5: The module of any one of the preceding aspects, further comprising a CO2 dispenser that is configured to intermittently dispense CO2at the tray when received within the receiving area. Attorney Docket: 36446.0365P1 Aspect 6: The module of any one of the preceding aspects, further comprising an air curtain that is configured to contain insect samples on the tray when received within the receiving area. Aspect 7: The module of any one of the preceding aspects, further comprising a vibrator that is configured to vibrate the tray sufficiently to inhibit movement of the insect samples. Aspect 8: The module of any one of the preceding aspects, further comprising the tray received within the receiving area, wherein the tray comprises a boundary defined by a color change that is configured to inhibit movement of insect samples thereacross. Aspect 9: The module of any one of the preceding aspects, further comprising the tray received within the receiving area, wherein the tray comprises a boundary defined by grease that is configured to inhibit movement of insect samples thereacross. Aspect 10: The module of any one of the preceding aspects, further comprising the tray received within the receiving area, wherein the tray comprises a liquid moat that is configured to inhibit movement of insect samples thereacross. Aspect 11: The module of aspect 10, wherein the liquid moat comprises water or alcohol. Aspect 12: The module of any one of the preceding aspects, further comprising the tray received within the receiving area, wherein the tray comprises a component causing a vibratory motion to evenly disperse the insects on the tray. Aspect 13: The module of aspect 12 wherein the surface of the tray is textured to aid in achieving the desired distribution of insects when vibrated. Aspect 14: The module of any one of the preceding aspects, 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. Aspect 15: The module of aspect 14, wherein the at least one end effector comprises a restrictor that is configured to restrict airflow therethrough to reduce an operative area of the vacuum applied at the outlet. Aspect 16: The module of aspect 15, wherein the restrictor comprises a tubing having a select cross sectional area. Aspect 17: The module of any one of aspects 14-16, wherein the at least one end effector comprises a screen positioned over the outlet of the at least one end effector. Attorney Docket: 36446.0365P1 Aspect 18: The module of any one of aspects 14-17, wherein the at least one end effector is further configured to apply a positive pressure to release the individual insect sample. Aspect 19: The module of aspect 18, wherein the at least one end effector comprises a cover that is configured to cover at least a portion of a top opening of a well of the lab plate. Aspect 20: The module of aspect 19, wherein the cover comprises a ring that circumferentially surrounds the outlet of the at least one end effector. Aspect 21: The module of aspect 20, wherein the cover of the at least one end effector comprises at least one groove that permits exhaust of gas therethrough while inhibiting movement of the individual insect sample therethrough. Aspect 22: The module of any one of aspects 14-21, wherein the at least one end effector comprises a plurality of end effectors. Aspect 23: The module of aspect 22, wherein the robot is configured to vertically articulate the position of each end effector relative to each other end effector of the plurality of end effectors. Aspect 24: The module of any one of aspects 14-23, further comprising a robot cleaning assembly that is configured to clean the at least one end effector. Aspect 25: The module of aspect 24, wherein the robot cleaning assembly comprises a spray nozzle, a brush, or a combination thereof. Aspect 26: The module of any one of aspects 1-11, wherein the robot comprises at least one end effector having an outlet, wherein the robot is configured to form, at the outlet of the end effector, a drop of liquid that is sufficient to adhere to the individual insect sample. Aspect 27: The module of aspect 26, wherein the robot is further configured to aspirate the liquid into the outlet to pull the individual insect sample therein. Aspect 28: The module of aspect 27, wherein the robot is further configured to dispense the liquid with the individual insect sample therein into the lab plate. Aspect 29: The module of aspect 28, wherein the robot is further configured to expel any remaining external fluid with compressed air Aspect 30: The module of any one of aspects 1-11, wherein the robot comprises at least one end effector that is configured to selectively apply a static electricity charge to releasably hold the individual insect sample thereto. Aspect 31: The module of any one of aspects 1-11, wherein the robot comprises at least one end effector, the at least one end effector comprising: Attorney Docket: 36446.0365P1 a brush that is configured to adhere to the individual insect sample; an air source; and a blow-off tube that is configured to receive air from the air source, the blow-off tube having an outlet that is positioned to blow the individual insect sample from the brush when the air source supplies air to the blow-off tube. Aspect 32: The module of any one of the preceding aspects, wherein the computing device is configured to apply blob detection to detect the location of the individual insect sample of the plurality of insect samples on the tray. Aspect 33: The module of any one of the preceding aspects, wherein the computing device is configured to apply a machine learning algorithm to detect the location of the individual insect sample of the plurality of insect samples on the tray. Aspect 34: The module of any one of the preceding aspects, wherein the image processing system is configured to provide real-time insect sample location data to accommodate for insect sample movement. Aspect 35: The module of any one of the preceding aspects, wherein the image processing system is configured to provide real-time determine a number of insect samples remaining on the tray. Aspect 36: The module of any one of the preceding aspects, further comprising a leaf sample excisor that is configured to cut a portion of a leaf and position the portion of the leaf within a well of the lab plate. Aspect 37: The module of any one of the preceding aspects, wherein the lab plate comprises a plurality of openings that are configured to receive the predetermined number of insect samples, wherein the robot is configured to receive the insects from the predetermined plurality of openings. Aspect 38: A module comprising: a lab plate area for receiving a lab plate; and an insect sample dispenser that is configured to dispense insect samples into the lab plate within the lab plate area, the insect sample dispenser comprising: a receiving space that is configured to receive a plurality of insect samples; a metering tray having a plurality of holes that are configured to receive respective insect samples of the plurality of insect samples from the receiving space, Attorney Docket: 36446.0365P1 wherein the plurality of holes are arranged to be positioned above respective wells of the lab plate; a slide gate that is configured to slide relative to the metering tray to release the insect sample within the plurality of holes into the lab plate within the lab plate area. Aspect 39: A module comprising: a CO2 supply that is configured to sedate a plurality of insects; and a feeder that is configured to distribute the sedated insects. Aspect 40: The module of aspect 39, wherein the feeder comprises a ladder feeder. Aspect 41: The module of aspect 36, wherein the feeder comprises a vibratory conveyor. Aspect 42: The module of aspect 36, wherein the feeder comprises a metering device that is configured to separate from the plurality of insects a predetermined number of insect samples. Aspect 43: The module of aspect 42, wherein the predetermined number of insect samples is a single insect sample. Aspect 44: The module of aspect 42, wherein the metering device comprises a rotating wheel, wherein the rotating wheel defines an opening that is configured to receive therein the predetermined number of insect samples. Aspect 45: The module of aspect 42, wherein the metering device comprises a tray having a plurality of openings that are configured to receive the predetermined number of insect samples. Aspect 46: The module of aspect 45, wherein the tray comprises a plurality of openings that are arranged to overlie respective wells of a lab plate. Aspect 47: The module of aspect 46, further comprising: a conveyor that is configured to position the lab plate over the tray; and a rotation device that is configured to rotate an arrangement of the tray and the lab plate so that the predetermined number of insect samples dump from the plurality of openings of the tray into the respective wells of the lab plate. Aspect 48: A system comprising: at least one module as in any one of the preceding aspects; and Attorney Docket: 36446.0365P1 a service robot that is configured to transport the tray to and from the at least one module. Aspect 49: The system of aspect 48, further comprising a sealer that is configured to seal the lab plate to contain the insect sample within the lab plate, wherein the service robot is configured to transport the lab plate to the sealer. Aspect 50: The system of any one of aspects 48-49, further comprising a piercer that is configured to pierce the lab plate to form holes that permit air exchange but inhibit movement of insect samples therethrough. Aspect 51: The system of aspect 50, wherein the piercer is configured to form holes having a diameter of no greater than 0.35 mm. Aspect 52: The system of aspect 50, wherein the piercer is configured to simultaneously pierce a plurality of holes in the lab plate. Aspect 53: The system of aspect 50, wherein the piercer is configured to interchangeably use different piercing elements that form holes of different sizes. Aspect 54: The system of aspect 50, wherein at least one module comprises a plurality of modules as in any one of the preceding aspects. Aspect 55: A method comprising: distributing at least one insect sample into a lab plate using a module as in any one of aspects 1-47. Aspect 56: The method of aspect 55, wherein the at least one insect sample is an egg or a larva. Aspect 57: The method of aspect 55, further comprising tumbling the insect samples with a plurality of granules so that the at least one insect sample adheres to respective granules, wherein distributing the at least one insect sample into the lab plate comprises lifting individual granules of the plurality of granules with the at least one insect sample adhered thereto. Although the foregoing embodiments of the invention have been described in some detail by way of illustration and example for clarity of understanding, certain changes and modifications are encompassed within the scope of the appended claims.

Claims

Attorney Docket: 36446.0365P1 The following is claimed:

1. A module comprising: a receiving area for receiving a tray having thereon a plurality of insect samples; an imaging processing system configured to identify a location of an individual insect sample of 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 when received within the receiving area; and a computing device in communication with the camera; a lab plate area for receiving a lab plate; and a robot that is configured to: pick up the 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 lab plate in the lab plate area.

2. The module of claim 1, further comprising a cooling system that is 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 a high thermal conductivity.

5. The module of claim 1, further comprising a CO2dispenser that is configured to intermittently dispense CO2 at the tray when received within the receiving area.

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

7. The module of claim 1, further comprising a vibrator that is configured to vibrate the tray sufficiently 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 boundary defined by a color change that is configured to inhibit movement of insect samples thereacross.Attorney Docket: 36446.0365P1 9. The module of claim 1, further comprising the tray received within the receiving area, wherein the tray comprises a boundary defined by grease that is configured to inhibit movement of insect samples thereacross.

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

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 causing a vibratory motion to evenly disperse the insects on the tray.

13. The module of claim 12 wherein the surface of the tray is textured to aid in achieving the desired distribution of 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 restrictor that is configured to restrict airflow therethrough to reduce an operative area of the vacuum applied at the outlet.

16. The module of claim 15, wherein the restrictor comprises a tubing having a select cross sectional area.

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

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

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

20. The module of claim 19, wherein the cover comprises a ring that circumferentially surrounds the outlet of the at least one end effector.Attorney Docket: 36446.0365P1 21. The module of claim 20, wherein the cover of the at least one end effector comprises at least one groove that permits exhaust of gas therethrough 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 articulate the 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 robot cleaning assembly that is configured to clean the at least one end effector.

25. The module of claim 24, wherein the robot 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 outlet, wherein the robot is configured to form, at the outlet of the end effector, a drop of liquid that is sufficient to adhere to the individual insect sample.

27. The module of claim 26, wherein the robot is further configured to aspirate the liquid into the outlet to pull 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 the lab 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 that is configured to selectively apply a static electricity charge to releasably hold the individual insect sample thereto.

31. The module of claim 1, wherein the robot comprises at least one end effector, the at least one end effector comprising: a brush that is configured to adhere to the individual insect sample; an air source; andAttorney Docket: 36446.0365P1 a blow-off tube that is configured to receive air from the air source, the blow-off tube having an outlet that is positioned to blow the individual insect sample from the brush when the air source supplies air to the blow-off tube.

32. The module of claim 1, wherein the computing device is configured to apply blob detection to detect the 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 the 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 for insect sample movement.

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

36. The module of claim 1, further comprising a leaf sample excisor that is configured to cut a portion of a leaf and position the portion of the leaf within a well of the lab plate.

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

38. A module comprising: a lab plate area for receiving a lab plate; and an insect sample dispenser that is configured to dispense insect samples into the lab plate within the lab plate area, the insect sample dispenser comprising: a receiving space that is configured to receive a plurality of insect samples; a metering tray having a plurality of holes that are configured to receive respective insect samples of the plurality of insect samples from the receiving space, wherein the plurality of holes are arranged to be positioned above respective wells of the lab plate; a slide gate that is configured to slide relative to the metering tray to release the insect sample within the plurality of holes into the lab plate within the lab plate area.Attorney Docket: 36446.0365P1 39. A module comprising: a CO2 supply that is configured to sedate a plurality of insects; and a feeder that is configured to distribute 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 that is configured to separate from the plurality of insects a predetermined number of insect samples.

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 that is configured to receive therein the predetermined number of insect samples.

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

46. The module of claim 45, wherein the tray comprises a plurality of openings that are arranged to overlie respective wells of a lab plate.

47. The module of claim 46, further comprising: a conveyor that is configured to position the lab plate over the tray; and a rotation device that is configured to rotate an arrangement of the tray and the lab plate so that the predetermined number of insect samples dump from the plurality of openings of the tray into the respective wells of the lab plate.

48. A system comprising: at least one module as in any one of claims 1-37; and a service robot that is configured to transport the tray to and from the at least one module.Attorney Docket: 36446.0365P1 49. The system of claim 48, further comprising a sealer that is configured to seal the lab plate to contain the insect sample within the lab plate, wherein the service robot is configured to transport the lab plate to the sealer.

50. The system of claim 48, further comprising a piercer that is configured to pierce the lab plate to form holes that permit air exchange but inhibit movement of insect samples therethrough.

51. The system of claim 50, wherein the piercer is configured to form holes having a diameter of no greater than 0.35 mm.

52. The system of claim 50, wherein the piercer is configured to simultaneously pierce a plurality of holes in the lab plate.

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 at least one module comprises a plurality of modules as in any one of the preceding claims.

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

56. A method comprising: distributing at least one insect sample into a lab plate using a module as in any one 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 tumbling the insect samples with a plurality of granules so that the at least one insect sample adheres to respective granules, wherein distributing the at least one insect sample into the lab plate comprises lifting individual granules of the plurality of granules with the at least one insect sample adhered thereto.Attorney Docket: 36446.0365P1 59. A method comprising: distributing at least one insect sample into a lab plate using a module as in any one of claims 39-47.

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