Crop monitoring and protection

Abstract

The present teachings relate to a computer-implemented method comprising receiving, at a processing means, crop data indicative of a type of crop, receiving, at the processing means, area data indicative of at least one pest that may be present at or near the location of the crop, and selecting, using the processing means, from a database, a sensor configuration suitable for selectively genotyping at least one relevant pest, wherein the at least one relevant pest is among the at least one pest, the selection being performed in response to the crop data and the area data. The present teachings also relate to an electronic device and a computer software product comprising a processing means configured to perform the disclosed steps.

Description

[Technical Field]

[0001] The present teachings generally relate to computer-aided identification of organisms that affect crops. The present teachings also relate to computer-aided management and control of one or more such organisms. [Background technology]

[0002] Harmful organisms such as weeds and plant pests such as parasitic bacteria, fungi, insects, mites, nematodes, viruses, viroids, etc. can cause significant damage to crops. Chemical compounds or biological preparations such as herbicides, fungicides, insecticides, acaricides, or nematicides are used in agriculture to control such weeds and plant pests, and are commonly known as pesticides.

[0003] It is known that some harmful organisms may respond to insecticidal chemical or biological compounds by evolving and developing resistance to the compounds to which they are exposed. For example, the organism may produce mutants that are highly resistant or resistant to the applied insecticide compound. The organism may even become resistant to other insecticide compounds; this is a phenomenon commonly known as cross-resistance. Therefore, such insecticide compounds may become ineffective against resistant organisms. Therefore, applying such compounds and other compounds with cross-resistance to control resistant organisms may be ecologically and economically undesirable. As mentioned above, organisms may acquire resistance, for example, by mutation or by shift.

[0004] WO19149626 proposes the control of resistant pests based on DNA / RNA sequencing technology.

[0005] Therefore, there is a need for cost-effective methods for the field identification of organisms. Summary of the Invention

[0006] It is intended that at least some of the problems inherent in the prior art be solved by the subject matter of the appended independent claims.

[0007] According to a first aspect of the present teachings, there can be provided a computer-implemented method comprising: · receiving, at the processing means, crop data indicative of a type of crop; · receiving, at said processing means, area data indicative of at least one pest that may be present at or near the location of said crop; using said processing means to select from a database a sensor configuration suitable for selectively genotyping at least one relevant pest, said at least one relevant pest being included among said at least one pest, said selection being performed in dependence on said crop data and said area data.

[0008] The described embodiments and preferred embodiments of the computer-implemented method are also valid for other objects of the invention, in particular electronic devices and computer programs, and it will be understood that they will not be repeated for each and every aspect of the invention, but may be applied mutatis mutandis.

[0009] The terms "resistant" and "resistance", mutatis mutandis, when used in "insecticide-resistant pests" and "fungicide-resistant", refer to the property of such a characterized organism that has reduced susceptibility to treatment with an insecticide. Susceptibility can be measured by the desired effect that the insecticide treatment is intended to achieve, such as growth regulation, prevention of infestation, pre-emergence control, control of existing pests, etc. Susceptibility is usually measured by the mortality rate of the pest when treated with an insecticide compared to when the pest is not treated. When the treatment is carried out pre-emergence (e.g., on crop plants or on a site such as soil where plants are growing or will grow), susceptibility is measured by the rate of pest emergence compared to when untreated. When referring to "resistant pests" or "resistance" herein, the susceptibility of insecticide-resistant pests is at least 10% lower, preferably at least 20% lower, more preferably at least 50% lower, and especially at least 80% lower than when controlled.

[0010] The terms "in situ" and "on-site" are equivalent and relate to a situation in which genotyping measurements are performed at the location where the sampled material is collected and / or prepared for measurement. The same location includes both the immediate vicinity of the location where the sampling was performed (e.g., within a 1000-meter radius, within a 100-meter radius, etc.), but also relates to the functional or organizational unit where the sampling was performed, such as a farm, breeding station, laboratory, greenhouse, etc. Thus, the terms "in situ" and "on-site" exclude any techniques that require the transport of samples to specialized analytical facilities, such as sequencing techniques. Thus, the terms "in situ" and "on-site" refer to a situation in which the method steps from sampling to the generation of a result signal or a processed result signal are performed over a period of up to 24 hours, preferably up to 3 hours, more preferably up to 1 hour, and particularly preferably up to 30 minutes.

[0011] The term "surroundings", when used in "around the crop location", relates to the immediate vicinity of where sampling was carried out (e.g., within a 1000 meter radius, within a 100 meter radius, etc.), but may also relate to the functional or organizational unit where sampling was carried out, such as the farm, neighboring farms, breeding station, laboratory, greenhouse, etc., as well as to regional units of the location of the crop plants, and neighboring regional units that share the same or comparable environmental conditions, e.g., weather conditions including precipitation, solar radiation intensity, soil type, altitude, central temperature, etc. Typically, the term "surroundings" relates to a radius of up to 50 km, preferably up to 10 km, from the location where the crop plants are growing or intended to be grown.

[0012] The term "locus" as used in "locus of crop (plant)" relates to the soil, area, material or environment in which the crop plant is growing or can be grown.

[0013] The terms "active" and "present" in the context of pests are equivalent. It will be understood that the choice of term will depend on the type of pest. For example, undesirable vegetation such as weeds is usually described as present, while mobile animals such as insects are described as active.

[0014] The term "associated" as used in the context of "associated pests" relates to a subgroup of pests, or more precisely, a subgroup of pests that area data indicates is unlikely to occur and that have an adverse effect on the particular crop of interest.

[0015] The terms "via a processing means" and "using a processing means" are equivalent and relate to a situation where an operation is performed automatically by a processing means, i.e. without further user interaction with the processing means.

[0016] Applicant has realized that by combining crop data with area data, the detection range for pests that may be present at a location or site can be significantly narrowed to focus on one or more relevant organisms. Furthermore, genotyping techniques can be subsequently applied to this narrowed range of interest to gain further advantages, as outlined below.

[0017] It will be understood that a crop is a product, typically a plant, that can be grown and harvested for profit or survival. Crops are grown in agriculture or aquaculture. Crops can be grown indoors or outdoors. Crops can be food crops, such as food grains, forage crops, food seeds, fruits or vegetables, or non-food crops, such as horticultural plants, turf or industrial crops (e.g., biofuels, fiber, etc.).

[0018] Even at the individual level of each of the two data, the detection scope can be limited according to relevance. For example, crop data including information related to the crop, such as the type of crop, can be used to limit the detection scope to a subset of pests that affect the target crop. As a further example, if the type of crop is wheat, pests that only affect potatoes can be excluded from the scope of testing. It will be appreciated that certain pests may only affect certain varieties or subtypes of crops. For example, a particular organism may affect the wheat variety "Spelt" but not another wheat variety "Durum." In such cases, the crop data may include information related to a specific species or species to further narrow the scope of testing.

[0019] It will be appreciated that the crop data may relate to crops already present on the site or may relate to crops planned to be grown on the site.

[0020] Similarly, the area data provides information regarding one or more pests that may be present at or near the location of the crop. In one embodiment, the area data includes information regarding the occurrence of relevant pests or parts thereof (e.g., spores) at or near the location of the crop. In another embodiment, the area data includes information regarding the occurrence of relevant pests or parts thereof (e.g., spores) around the location of the crop. For example, if soybean rust and powdery mildew incidents are detected within a certain distance from the crop, these two types of pests can be used to define the range of testing. A specific distance means within the same area, e.g., 1 km from the crop. The exact distance or definition of the area depends on the desired likelihood or certainty of the test results and is not limited to the scope or generality of the present teachings. For example, if a powdery mildew incident is reported within 50 m of the crop, the same pest, i.e., powdery mildew, is more likely to affect at least a portion of the crop than if the pest were detected, e.g., 1 km away from the crop.

[0021] The area data providing information related to one or more pests that may be active or present at or near the crop location also means that the area data may also include information about resistance genes of one or more pests that may be active or present at or near the crop location. For example, the area data may provide information about the presence of insecticide-resistant pests at or near the crop location, preferably about pesticides to which the pest is resistant, and more preferably about the presence of genes and / or mutations responsible for said resistance. Such information may also be related to previous growing seasons, i.e., the area data may include information about the presence of insecticide-resistant pests at or near the crop location during previous growing seasons, preferably about pesticides to which the pest is resistant, and more preferably about the presence of genes and / or mutations responsible for said resistance.

[0022] In one embodiment, the area data provides information regarding the type of pesticide resistance of pests present at or near the crop location, hi another embodiment, the area data provides information regarding the type of pesticide resistance of pests that were present at or near the crop location during the previous growing season.

[0023] The area data may include information regarding past physical and / or chemical treatments of the crop or the farm where the crop is growing or intended to be grown. Physical treatments relate, for example, to the application of agricultural machinery, weed burning, weed soaking, etc. Chemical treatments relate to the application and dosage of previous treatments with pesticides, such as fungicides, insecticides, or herbicides. In one embodiment, the area data includes information regarding pesticide applications, such as pesticides used and application rates, in previous growing seasons, preferably in previous and current growing seasons. It will be recognized that resistant pests are more likely to occur in areas that favor biological selection of resistant populations of pests. Therefore, information regarding previous chemical treatments can be used to assess the risk of pesticide resistance in or around the area where the crop is growing or intended to be grown.

[0024] When the area data includes information about the occurrence of a relevant pest, the method of the present invention is typically applied to detect DNA sequences that exhibit insecticide resistance (e.g., fungicide resistance), as described hereinbelow. A DNA sequence exhibits insecticide resistance if it functionally conveys insecticide resistance, for example, by conferring the ability to degrade chemicals to the pest, or if it does not functionally interfere with the interaction of the pesticide with the organism but is a marker for other DNA sequences that functionally interfere with the interaction of the pesticide with the organism. Thus, the method can be applied to detect DNA sequences that exhibit insecticide resistance, and the sensor configuration is configured to detect DNA sequences that exhibit insecticide resistance.

[0025] Thus, combining crop data with area data synergistically improves testing coverage by mutually excluding crop types and pests that are not associated with the other of the two data. Thus, the processing means can use the crop data and area data to automatically find the sensor configuration requirements from the database that are most suitable for selectively detecting the presence of one or more pests associated with the crop. It will be understood that selectively detecting means specifically detecting or testing for the presence of a given associated pest.

[0026] It will be understood that "at least one associated pest is from at least one pest" means that at least one pest is also represented by the area data. Thus, each associated pest is associated with a crop and is also represented by the area data.

[0027] It will further be appreciated that the selection is performed in response to the crop data and the area data, whereby the combination of the crop data and the area data indicates at least one relevant pest. According to one embodiment, the crop data indicates harmful organisms that may affect the crop. This can speed up the determination of the scope of the test, i.e., determining which pests are relevant to the detection, by defining an organism as a relevant pest if the pest is also indicated in the area data. Alternatively, the processing means can determine the scope of the test by combining the crop data with the area data and accessing additional data from the database to determine the scope of the test. Thus, the processing means can select a sensor configuration suitable for selectively genotyping at least one relevant pest from the database. The additional data can be, for example, data regarding pests that may affect the crop.

[0028] In one embodiment, the area data indicative of at least one pest does not include information regarding the occurrence of at least one (associated) pest or part thereof (such as a spore) that may be present at or around the crop location, and in particular does not include data based on analytical measurements or observations performed on at least one (associated) pest at or around the crop location, such as data obtained from visual identification means (e.g., microscopic detection) or genetic identification means (e.g., PCR-based or sequencing techniques). In another embodiment, the area data does not include any information regarding the identity of at least one (associated) pest that may be present at or around the crop location, and preferably does not include any information regarding the genetic identity of at least one (associated) pest.

[0029] According to another embodiment, the area data comprises seasonal data, which comprises information about the season that is used by the processing means to further refine the number of pests of concern to the crop.

[0030] According to another embodiment, the area data can include weather data, which can be used by the processing means to further determine which of the pests are more likely to be relevant given the prevailing conditions in the crop. Those skilled in the art will appreciate that prevailing conditions such as wind strength, wind direction, humidity, and ambient temperature are further factors that can determine the likelihood of a crop being distant from a previously reported incident of a pest. The prevailing conditions may be one or more of temperature, humidity, wind speed, wind direction, and precipitation, either at a given time and / or within a predetermined period of time.

[0031] Thus, seasonal and / or weather data may further be used to pre-select / select at least one relevant pest or at least one target pest.

[0032] According to a further aspect, the area data includes location data relating to the geographic location of the crop, such that the processing means is able to use the area data to determine the geographic location of the crop.

[0033] According to one embodiment, the processing means automatically retrieves any of the following from one or more databases in response to determining the geographic location of the crop: information regarding one or more pests that may be active or present at or near the location of the crop, seasonal data, and weather data.

[0034] Such other data within the area data, for example weather data and / or seasonal data and / or location data, may therefore further enable the processing means to narrow the scope of the test by further excluding harmful organisms that may affect the crop but that are not relevant to the test given the other data.

[0035] In one embodiment, the region data is selected from seasonal data, weather data, and location data. In another embodiment, the region data includes seasonal data. In another embodiment, the region data includes weather data. In another embodiment, the seasonal data includes location data.

[0036] The sensor configuration may be a single device capable of selectively detecting each of the relevant organisms to be tested, or it may be multiple devices or sensors, each individually designed to selectively detect a specific organism. Each sensor may include a specific assay to detect a specific organism. In this context, organisms with at least one single nucleotide polymorphism are considered different organisms. For example, the sensor configuration may be a single lab-on-chip device with a specific assay or capability for detecting each of the relevant organisms. Alternatively, the sensor configuration may be multiple different lab-on-chip devices, each capable of detecting a single specific organism different from the other devices. The sensor configuration may be a combination of a lab-on-chip device capable of detecting multiple different organisms and one or more lab-on-chip devices, each capable of detecting a different specific organism.

[0037] The database can include data from multiple test results and field tests. For example, the database can include probe data for each pest type or specific mutation measured in field tests and laboratory studies. Thus, the database can include selectivity data for each sensor, indicating the specific organisms that each individual sensor can detect. The processing means can then select an appropriate sensor configuration that best suits the requirements. In other words, the processing means selects a sensor configuration that can reliably and selectively detect each of the pests determined to be relevant by the processing device.

[0038] As a further example, the selectivity data may be one or more of nucleic acid data, protein data, or molecular data indicative of a specific organism. The sensor configuration may include a sensor based on nanopore technology. An advantage of nanopore technology is its quantitative capabilities. According to another embodiment, the sensor configuration may be based at least in part on microarray technology. An advantage of a microarray-based configuration is its multiplexing capabilities. According to yet another embodiment, the sensor configuration may be based at least in part on a graphene biosensor. Graphene biosensors may have higher sensitivity and / or selectivity, which may provide a sensor configuration that is more selective and / or capable of detecting smaller, trace amounts of organisms (e.g., a CRISPR Cas-modified graphene sensor system). High selectivity may be used, for example, to detect more specific strains or mutations. According to another embodiment, or in addition to the above, the sensor configuration may be at least in part a microfluidic device. According to another embodiment, the sensor configuration may be at least in part a high-speed PCR system, for example, based on a rapid temperature cycling system or isothermal amplification (e.g., a loop-mediated isothermal amplification system). The selected sensor configuration may be a combination of the above technologies. The processing means can then select an appropriate sensor configuration based on one or more of these techniques that best suits the current application, as determined by combining the crop data with the area data. According to another embodiment, the sensor configuration is selected based on sensor availability. For example, if two or more different types of sensors can selectively detect the same organism of interest, the processing means can select the sensor configuration based on given criteria. For example, the criteria by which the sensor is selected can be based on which sensor is available or most quickly available. According to another embodiment, the selection criteria may alternatively or in combination with the above include the cost of the sensor or sensor configuration used. Therefore, cheaper sensors are preferred over more expensive sensors. Furthermore, alternatively or in combination with the above, the selection criteria may include sensing speed. Thus, faster sensors can be selected over slower sensors.It will be appreciated that by having all or some of the above criteria and data available in the database or in another database coupled to the database, the processing means can select a sensor configuration optimized for detecting at least one relevant pest based on the given criteria and further based on a given priority of the criteria.

[0039] The crop data may be input provided at least in part by a user, or may be acquired automatically using a crop sensor, e.g., an image recognition system. The image recognition system may include an image sensor, such as a camera. By analyzing images of the crop captured by the image sensor, the processing means may automatically determine the type of crop. The image analysis may include comparing data from the captured images with an image library or database. The crop data may also be acquired automatically by analyzing biological samples from the crop, e.g., by analyzing parts of the crop, such as leaves, pollen, or roots.

[0040] Similarly, the area data may be, at least in part, input provided by a user. Alternatively, the area data may be, at least in part, retrieved from an area database or a remote server. For example, after determining the location of the crop, either via user input or automatically via geographic location means (e.g., GPS and / or beacons, etc.), the processing means may automatically retrieve information from a public database about which pests may be present at or near the crop location. According to one embodiment, the area data includes previous measurements of pests performed at or near the crop location, e.g., detection or no-detection measurements from previous growing seasons.

[0041] Different types of organisms can affect crops in different ways. If an organism affects the crop, i.e., has a negative effect on the crop, it can be called a pest. Alternatively, if an organism has a positive effect on the crop, it can be called a benign organism.

[0042] It will be understood that a pest can be any of the following: an infectious microorganism or bacterium, or a microorganism that affects a crop, such as a fungus, bacteria, virus, viroid, oomycete, nematode, protozoan, phytoplasma, insect, or the like. The term "pest" includes harmful organisms and weeds that can affect the health of a crop at any stage of cultivation. In this disclosure, the terms "pest and pathogen" are used interchangeably, and thus the term "pathogen" includes weeds and pests that affect a crop. Pests either affect a crop directly or indirectly. For example, ants are known to interact with aphids. Therefore, ants may be considered harmful organisms to a crop, even if they do not attack the crop directly but tend to attack the aphids that cause damage to the crop. Typically, the term "pests" relates to undesirable vegetation (e.g. weeds), fungi, arthropods (e.g. insects and arachnids), mollusks (e.g. snails), nematodes, viruses, and bacteria, preferably fungi (e.g. Septoria sp.).

[0043] It is further understood that the proposed method can be extended to the detection of one or more benign organisms. It will be understood that a benign organism may have a positive direct or indirect effect on a crop. That is, a benign organism may have an indirect effect on a crop rather than a direct effect on the crop. For example, if a first organism does not have a direct effect on a crop but has an effect or a negative effect on a second organism, and the second organism is a harmful organism to the crop, the first organism is considered to be benign to the crop.

[0044] Some consumers may prefer the use of biological alternatives to chemical active ingredients. For example, in organic agriculture, the use of naturally occurring substances is desirable, and the use of synthetic substances is avoided or strictly limited. Biological variants of chemical-based pesticides, i.e., biopesticides, may be more desirable in some markets. Such biological variants often rely on benign organisms, such as benign microorganisms. Biocontrol is another term used to refer to methods of using benign organisms to control pests, mites, weeds, and plant diseases. Treatments using such benign organisms are based on either the organisms applied to or around the crop, and / or extracts or portions of such benign organisms. For effective biocontrol treatments, it is beneficial to monitor the spread of benign organisms on or around the crop. As mentioned above, the benign organisms may be applied live and / or may be portions or extracts of the applied benign organisms.

[0045] Thus, according to one embodiment, the sensor configuration is selected by the processing means such that the configuration is also suitable for selectively genotyping at least one benign organism.

[0046] Information about at least one benign organism may be provided via the area data or as a separate input to the processing means. The processing means can then select a sensor configuration capable of detecting at least one benign organism. As outlined above, the detection of benign organisms may be performed via a single lab-on-a-chip device or via different devices, each of which may specifically target a particular different organism or may be performed by any combination of devices suitable for detecting related organisms.

[0047] Viewed from another aspect, the method comprises: · outputting information relating to the appropriate sensor configuration via a human machine interface operatively coupled to the processing means.

[0048] The information regarding suitable sensor configurations is typically an indication of what type of sensor configuration should be used, and may also relate to information regarding the combination of sensor configurations that should be used.

[0049] Thus, the processing means can output information regarding the selected sensor configuration via a human-machine interface (HMI). Thus, the user can learn about the configuration of the specific lab-on-a-chip device or devices to be used for testing. The human-machine interface ("HMI") can include a video display unit (e.g., an LCD (liquid crystal display), a CRT (cathode ray tube) display, or a touchscreen), an alphanumeric input device (e.g., a keyboard), a cursor control device (e.g., a mouse), and / or a signal generation device (e.g., a speaker). Thus, the HMI can be, for example, a visual interface such as a screen and / or an audio interface such as a speaker. Thus, output can be displayed to the user and / or announced via the speaker. Thus, the process of selecting and using the correct device for detecting the relevant pest can be made simple and intuitive for the user. For example, the devices can be intuitively coded using color codes. Thus, the HMI can output to the user that the "blue" device should be used for detection. The user can then select the blue device and use it to perform the detection. This is particularly beneficial when several different types of devices are available for detecting different pests. Alternatively or additionally, the devices may be coded with an alphabetic, numeric, or alphanumeric code suitable for recognizing the correct device in a user-friendly manner. For example, the devices may be designated according to a respective scheme, i.e., device "1," device "A," or device "1B." According to one embodiment, the devices may be in the form of cartridges used for specific detection or for a given number of organisms.

[0050] When the selected sensor configuration is used, measurements are selectively performed on relevant organisms or strains of interest selected by combining crop data with area data. As a result, significant additional time savings are achieved compared to sequencing the entire spectrum of strains. Therefore, by combining crop and area data to select one or more relevant pest strains, combined with specially selected genotyping sensors that selectively test for those relevant pest strains, the proposed method achieves multiple benefits, including time and cost advantages.

[0051] Sequencing is typically a resource-intensive activity because it requires sequencing the genome of an entire organism, thereby generating large amounts of unnecessary data.

[0052] Applicant recognizes that smaller regions of the genome carrying information about pathogens or specific pesticide resistance can be specifically targeted for detection by combining crop data with regional data. A particularly useful application of the methods of the present invention is detecting specific fungicide resistance in fungal diseases. The term "fungicide resistance" typically refers to the lower rate of fungal disease control that can be achieved by applying a specific fungicide at a given concentration compared to another (wild-type) population of the fungal disease. Fungicide resistance is generated by various mechanisms (e.g., selection by fungicide treatment, natural or induced mutation, or sexual recombination). At the molecular level, fungicide resistance is generated by adaptations of the protein targeted by the fungicide, such as the exchange, deletion, and / or addition of one or more amino acids in the primary sequence of the protein. These adaptations can be detected at the DNA level by various techniques.

[0053] The methods of the present invention preferably utilize targeted detection tools. Such targeted detection tools are specific for DNA sequences that indicate insecticide resistance or tolerance (e.g., fungicide resistance, such as single nucleotide polymorphisms ("SNPs")). Ideally, targeted detection tools are specific for exactly one DNA sequence. The results of targeted detection tools typically classify samples as "true" for samples containing the probed DNA sequence and "false" for samples that do not contain the probed DNA sequence. They also produce quantitative results, providing a value indicating the amount of DNA material containing the sequence to be detected. A combination of quantitative and qualitative information is also possible, such as classification information based on a predetermined threshold. In contrast to other DNA analysis tools, such as sequencing, targeted detection tools provide less information than non-selective DNA analysis tools, but are cheaper, less time-consuming, and easier to set up. Targeted detection tools significantly contribute to enabling field approaches because they require much less equipment and time while still producing the necessary information.

[0054] A typical method relies on hybridization of a probe oligonucleotide bearing the sequence to be detected in a pest (e.g., a fungus), or its reverse complement. The probe oligonucleotide, which may be RNA-based or DNA-based, is contacted with the DNA of the pest, e.g., a fungus, to be analyzed. If the pest has corresponding DNA that exhibits insecticide resistance, a single strand of the fungal DNA hybridizes with the probe oligonucleotide under appropriate conditions. Hybridization can be detected by various methods, such as polymerase chain reaction technology, fluorescence-based technology, luminescence-based technology, or electronic measurement (e.g., on a semiconductor chip such as a graphene chip).

[0055] Thus, the method of the present invention preferably comprises the steps of performing in situ genotyping of at least one relevant pest from an environmental sample obtained from the crop site using a sensor arrangement connected at the sensor interface, and generating a result signal at the sensor interface via the sensor arrangement, more preferably the sensor arrangement comprising a target-directed detection tool, most preferably a sequence-specific detection tool, particularly preferably a sequence-specific detection tool based on hybridization of a sequence to be identified with a probe DNA or RNA molecule, for example, where the sequence to be identified indicates insecticide resistance, e.g., fungicide resistance. Thus, the sensor arrangement is typically suitable for in situ genotyping of at least one relevant pest.

[0056] An advantage of the method of the present invention is that it allows for the detection of harmful organisms (e.g., fungal diseases) at a very early stage of plant infestation, before the disease becomes detectable by visual assessment of material (e.g., fungal material such as spores) or infected plants. This is particularly important for fungicide-resistant fungal strains, as these strains are difficult to control and the applicant is seeking specific measures. By taking into account crop and area data and performing a pre-selection of an appropriate sensor configuration, the method of the present invention can specifically test samples for the presence of the relevant harmful fungi. Because neither crop nor area data rely on visual detection of fungal diseases, the method of the present invention can be applied even when no visual information is yet available. Thus, the method of the present invention can be performed before detecting harmful organisms such as fungal diseases by visual assessment of the crop plants being analyzed.

[0057] The selection and use of such sensor configurations, for example in the form of one or more prefabricated sensors or lab-on-a-chip devices, may be more user-friendly.

[0058] Typically, information about the possible presence of pests in an area is available in some form, for example, through public announcements by authorities or monitoring groups, internet databases, etc. Such information can be obtained from results from surrounding crops or from previous results from the same crop, which may include sequencing results. Such information can be used to generate area data, which, when combined with crop data, can significantly reduce the number of points of interest in the pest spectrum, resulting in faster measurements and faster results.

[0059]

[0003] It is usually highly desirable to obtain results as quickly as possible, especially when harmful organisms are actually present, for example, to immediately initiate treatment to prevent further damage to the crop, or to initiate a treatment plan if the crop has not yet grown. Even if visual information of the presence of a harmful organism, or an outbreak, is available, it may not be sufficient to initiate treatment because there is a risk that the organism strain is resistant to the intended treatment. Therefore, rapid availability of test results is a significant advantage. The proposed solution at least helps to make relevant results available more quickly.

[0060] Thus, according to a further aspect, the method also comprises: · determining the genotype of at least one relevant pest from an environmental sample obtained from the crop site using a sensor arrangement connected to the sensor interface in situ and generating a result signal via the sensor arrangement at the sensor interface; processing the result signal using the processing means to determine whether one or more of the at least one associated pest is present in the environmental sample, thereby detecting the presence of any of the at least one associated pest on the crop site via the portable device.

[0061] More particularly, in a second aspect, there is also provided a method for in situ detection of the presence of pests in a crop field, the method being carried out using a portable electronic device comprising processing means and a sensor interface, the method comprising: receiving, at the processing means, crop data, the crop data indicating a type of crop; receiving area data with a processing means, the area data indicating the presence of one or more pests in an area in which the crop site is located; selecting, using processing means, from a database a sensor configuration suitable for selectively genotyping at least one relevant pest, said at least one relevant pest being among said at least one pest, said selection being performed in response to said crop data and said area data; - determining the genotype of at least one relevant pest from an environmental sample obtained from the crop site using a sensor arrangement connected to the sensor interface in situ, and generating a result signal via the sensor arrangement at the sensor interface; processing the result signal using the processing means to determine whether at least one associated pest is present in the environmental sample, thereby detecting the presence of any of the at least one associated pest on the crop site via the portable device.

[0062] Also, as outlined above, according to one embodiment, the sensor configuration is selected by the processing means such that the sequence is suitable for selectively genotyping at least one benign organism, the method comprising: · Processing the result signal using a processing means to determine whether at least one benign organism is present in the environmental sample.

[0063] Thus, the presence of at least one benign organism at a crop site can also be detected via the portable device. The benign organism can be, for example, a fungicide.

[0064] Thus, a method can be provided for providing rapid detection of relevant pests on a crop site. Thus, the present teachings provide a method for accounting for the type of organism according to the type of crop before determining whether it should be detected.

[0065] The resulting signals can be stored locally or uploaded to a remote server or database.

[0066] As mentioned above, the crop data may be obtained at least in part automatically, for example via an image recognition system. The area data may also be obtained at least in part from an area database or a remote server. According to another embodiment, further information regarding at least one relevant pest may be obtained from the image recognition system. For example, from the captured image, the processing means may at least in part determine the presence of an organism at the site by recognizing a distinctive feature caused by the presence of the organism. The feature may be a characteristic of the pest itself, such as the shape, size, or color of the pest, or a change in the appearance of the crop caused by the pest.

[0067] According to one embodiment, a combination of area data, crop data, and result signals is used to pre-construct sensor configurations for future seasons, thus providing appropriate sensing devices for the upcoming season based on detected organisms and local environmental conditions.

[0068] According to another aspect, the trajectory for collecting the environmental sample is determined via the processing means in response to area data, such as seasonal data. According to yet another aspect, the trajectory for collecting the environmental sample is determined in response to seasonal data and meteorological data or prevailing conditions. In this manner, the processing means can improve the sampling process by selecting a sampling location, or a series of sampling locations, or a sampling path, such that the organism of interest is more likely to be present in the environmental sample. It will be appreciated that, since it is desirable to detect an organism if it is actually present in the field, the present teachings can provide a method for achieving a higher reliability of such detection.

[0069] Another advantage of the proposed method is that it can be easily and quickly used by less technically savvy users, and that the analysis and result signals can be performed locally with reduced processing requirements. Therefore, the portable electronic device can be implemented as a portable or handheld device, or as a low-power battery-operated device. Yet another advantage is that, for example, a user (e.g., a farmer) can receive on-site advice on detection processes, crop treatments, and / or sampling processes without needing a high-speed network or Internet connection to upload sequencing results to the cloud, perform analysis by a remote server, and download recommended results to the electronic device to advise the user. In many cases, this can be a significant advantage, especially for locations with poor Internet connectivity, such as rural areas. The portable device can also perform the outlined functions entirely locally, possibly via a database stored on a local storage medium (e.g., computer memory) and by interpreting the result signals via the storage medium. In some cases, the database may be synchronized from such locations where a better network connection is available.

[0070] Thus, the present teachings can provide a method for systematically eliminating irrelevant detection regions in the pest organism spectrum, thereby focusing on relevant organisms. Detection results can thus be obtained within a time frame of minutes rather than hours. The proposed teachings are therefore particularly suitable for on-site detection, i.e., measurements or detections that are performed essentially at the location where environmental samples are collected. In other words, the present invention reduces the effort required to identify relevant pests.

[0071] As previously mentioned, a sensor configuration may be a single device capable of selectively detecting each of the relevant organisms, or it may be multiple sensors, each individually designed to selectively detect a different specific organism than the other sensors in the multiple sensors can detect. Multiple sensors may be simultaneously connectable at a sensor interface, or sequentially connectable, with any set of multiple sensors simultaneously connectable at a sensor interface to detect at least a subset of pests. For example, a sensor configuration may be a set of sensors connected sequentially or one at a time at a sensor interface, each sensor detecting a specific pest different from those the other sensors can detect. As a further example, multiple or all sensors may be simultaneously connectable at a sensor interface. Therefore, the particular type of interface is not limited to the scope of the present teachings.

[0072] Thus, more generally, a computer-implemented method may be provided that includes: · receiving, at the processing means, crop data indicative of a type of crop; · receiving, at the processing means, area data indicative of at least one organism that may be present at or near the location of the crop; using processing means to select from a database a sensor configuration suitable for selectively genotyping said at least one organism, said selection being performed in response to said crop data and said region data.

[0073] As mentioned before, the microorganism may be a benign organism or a harmful organism. The advantages of this method are similar to those in the above case, i.e., when the organism is a harmful organism. These advantages have already been discussed in detail. An advantage of this method when the organism is a benign organism is that the processing means can select an appropriate sensor from a database to detect the organism.

[0074] Similar to the first aspect, according to one embodiment, the method includes: · outputting information regarding the appropriate sensor configuration via a human machine interface operatively coupled to the processing means.

[0075] Further, similar to the first aspect, according to a further or alternative embodiment, the method includes: · determining the genotype of one or more organisms from environmental samples obtained from the crop field using a sensor configuration connected to the sensor interface in situ and generating a result signal via the sensor configuration at the sensor interface; processing the resultant signal using the processing means to determine whether one or more organisms are present in the environmental sample, thereby detecting the presence of any of the one or more organisms on the crop site via the portable device.

[0076] Thus, more particularly as in the second aspect, there can also be provided a method for in situ detection of the presence of an organism in a crop field, the method being carried out using a portable electronic device comprising processing means and a sensor interface, the method comprising: receiving, at the processing means, crop data, the crop data indicating a type of crop; receiving area data with a processing means, the area data indicating the presence of one or more organisms in an area in which the crop site is located; using said processing means to select from a database a sensor configuration suitable for selectively genotyping said at least one organism, said selection being performed in response to said crop data and said region data; · determining the genotype of one or more organisms from environmental samples obtained from the crop site using a sensor configuration connected to the sensor interface in situ and generating a result signal via the sensor configuration at the sensor interface; processing the resultant signal using the processing means to determine whether one or more organisms are present in the environmental sample, thereby detecting the presence of any of the one or more organisms on the crop site via the portable device.

[0077] As mentioned earlier, this microorganism may be a benign or harmful organism.

[0078] According to any of the foregoing aspects, the method further includes: · generating, via the processing means, a processing result signal indicative of the presence of one or more organisms in the environmental sample.

[0079] According to one embodiment, the processed result signal is generated by analyzing data, e.g., raw measurement data, from the result signal generated by the sensor arrangement, or the processed result signal may essentially be a copy of the result signal.

[0080] The term environmental sample encompasses any type of sample that can be used to detect the presence of an organism. Therefore, it is not necessary for an environmental sample to include a part of a crop. Thus, an environmental sample may or may not include a part of a crop. An environmental sample can be collected from any one or combination of air, water, inorganic, or organic matter from a crop site. For example, an environmental sample can be a soil sample obtained from a crop site, and / or leaves, or other organic matter, such as leaf litter, humus, or composted material, obtained from a crop site. An environmental sample can also be pollen collected on-site directly from the crop or from the air. Therefore, it is understood that an environmental sample can be any one or more of the following: a whole plant, a plant part, such as leaves, roots, flowers, pollen, a soil sample, or a spore collection (e.g., on filter paper). A foliage or leaf sample can include a part of a crop, or it can be weed leaves or plant parts of weeds. When a sample is collected from the air of a field, indicators of the organism, such as spores, traces, etc., are collected. In some cases, the sample can include organisms in the form of insects or parts thereof. According to one embodiment, a soil sample, or a sample of, for example, the remaining biomass of a crop or weed from a previous season, is used to identify at least one organism living or hibernating in the soil or biomass. Thus, the processing means can determine at least one pest that can infect the upcoming crop. In particular, to detect microorganisms, any of the aforementioned sample types can be collected alone or in combination for the detection of such microorganisms in the sample.

[0081] The selected sensor configuration is connected at a sensor interface that functionally connects at least one of the sensors of the sensor configuration to the processing means at a given time. The sensor interface may be a hardware interface, for example, when at least one of the sensors in the sensor configuration is in the form of a cartridge that is plugged into an electronic device that includes the processing means. The sensor configuration may have terminals or pins that form part of the sensor interface by functionally connecting at least a portion of the sensor configuration to the processing means at a given time. This is particularly advantageous when the electronic device is a handheld device, as it allows the sensor configuration to be attached to the handheld device, thus making it more convenient for on-site measurements.

[0082] In some cases, the sensor interface may be a wireless interface. Thus, at least one of the sensors may be functionally connected wirelessly to the processing means. This is particularly advantageous in cases where the electronic device is a larger unit, perhaps not suited to being a handheld device. In this case, a wireless sensor is advantageous as a lightweight unit for collecting environmental samples in the field and performing on-site detection of harmful organisms from these samples. Specific examples of wired and wireless sensors are provided in a non-limiting sense. Combinations of wired and wireless types are also possible. It will be understood that the specific implementation of such sensors is not essential to the scope or generality of the present teachings.

[0083] After an environmental sample is introduced into a sensor configuration selected for detection, the sensor performs in situ detection and, optionally, quantification when using an appropriate sensor configuration of one or more harmful organisms in the sample. The result of the detection is generated in the form of a result signal. The result signal can be a signal that includes outputs from different sensors in the sensor configuration, which is the case when multiple different organisms are detected. The sensor outputs can be generated simultaneously or at a different time than the generation of other sensor outputs, which is the case when the sensor configuration includes sensors that are sequentially connected to a sensor interface.

[0084] The processing means processes the result signals to determine whether one or more relevant pests are present in the environmental sample (i.e., whether they are detected by each sensor of the sensor configuration). As already understood, the one or more organisms are targeted organisms narrowed down by combining the crop data with the area data, especially if the one or more organisms are harmful organisms. Therefore, as discussed, the sensor configuration was selected based on both of these data. The proposed method can reduce detection time. Furthermore, combining the crop data with the area data can also save costs by using targeted sensors to detect specific types of pests found to be of interest, compared to using field-wide sequencing measurements, for example. The proposed teachings are therefore also suitable for realizing a map of organisms. The map can reflect one or more geographic distributions of one or more relevant organisms, i.e., at least one relevant pest and / or at least one benign organism. Thus, through the results collected from multiple detections performed over a given area, a map can be obtained that represents the spread of any one or more relevant organisms across the area. The map can be an interior map of a greenhouse or an aggregate map of multiple greenhouses. Greenhouses are enclosed or semi-enclosed structures used to grow crops such as vegetables and fruits. Such greenhouse maps can be used to track which greenhouses are infected with harmful organisms, e.g., resistant mutants or viruses, so that quarantine measures and / or treatments can be determined or planned.

[0085] Those skilled in the art will appreciate that faster detection as achieved by the proposed method allows for the generation of higher resolution maps. Furthermore, lower cost of sensors may also be an enabling factor for the generation of such detailed maps. Thus, according to another embodiment of any aspect, the method also includes: · generating field result data using the processing means by combining the processed result signal with location data of the crop field where the environmental sample was collected; · storing at least one of the field result data in a database; Combining data from the plurality of field result data to obtain a field map representing the territorial extent of any of one or more associated organisms.

[0086] The location data may be part of the area data or may be received from alternative sources, such as geolocation systems. Geographical location systems such as GPS and GALILIO are available as modules that can be integrated into handheld devices and / or lab-on-a-chip devices. In this manner, the processing means can obtain the location information of the crop site from such modules. Those skilled in the art will appreciate that alternative geolocation methods and devices, such as radio navigation technology, can also be applied. Any technology that provides adequate spatial resolution of location to distinguish between two area-separated environmental samples can be applied to the proposed teachings.

[0087] The field map obtained in this way allows for finer resolution of biodispersal data. This can have several advantages, for example, recognizing any variations in the population of organisms spreading across the field area. Thus, zones of high and / or low bioactivity can be identified. This can provide information, for example, about how effective a treatment was or even about the direction from which a harmful organism invaded. In some cases, it can even identify biotic hotspots and / or cold spots. By hotspots, we mean areas where one or more related organisms are more active than neighboring areas. In some cases, these hotspots are associated with locally dominant conditions at the respective sites where the organisms are more active. The ability to identify hotspots for related harmful organisms can enable appropriate treatment of such hotspots. Similarly, cold spots can also be identified. By cold spots, we mean areas where at least one of one or more related organisms is less active or less active than neighboring areas. In some cases, these cold spots are associated with locally dominant conditions at the respective sites where the organisms are less active. Biotic activity can be influenced by replicating such conditions. For example, in some cases, by replicating the locally prevailing conditions in a cold spot of a harmful organism, it is possible to reduce or even eliminate the harmful organism in the rest of the crop. Identifying one or more hot spots or cold spots of a related pest provides benefits in terms of reduced or more effective use of active ingredients such as pesticides, fungicides, insecticides, or herbicides. Similarly, identifying one or more hot spots or cold spots of a benign organism is beneficial for making overall biocontrol more effective.

[0088] General conditions that affect hot or cold spots can include any one or more of the following determinants: temperature, moisture level, pH value, sunlight and / or wind conditions.

[0089] Another important advantage of such maps is that they can provide a better overview of the density of the organisms and provide valuable information on the size of the infestation, which is important for planning the treatment of such areas according to the organisms' activity.

[0090] According to another aspect, the method further comprises: · Determining at least one site where another measurement or detection is required by analyzing the field map via the processing means.

[0091] The additional measurement may be the same type of detection as previously performed, or may be a different measurement, for example, a measurement of at least one parameter to determine the prevailing condition, or a recommended sequence measurement to obtain further information about the prevailing condition.

[0092] It will be appreciated that by implementing any of the above aspects of acquiring crop data and / or area data, the portable electronic device is at least partially automated. In some cases, the portable device may be fully automated, for example, in the form of a robot that performs multiple in-situ measurements at a crop location. For example, a computer-implemented method, including performing genotyping of organisms and generating result signals, can be performed online on an agricultural machine, such as a tractor. The result signals, processed result signals, or the generated map can then be simultaneously applied to adapt any agricultural measures implemented by the agricultural machine. For example, the machine can use information conveyed by these signals or maps to select pesticides and / or pesticide dosages across the entire field, or select pesticides and / or pesticide dosages according to the cold spots and hot spots, i.e., depending on the section of the field, in response to the information conveyed by the result signals, processed result signals, or map.

[0093] A robot (e.g., agricultural machine) can, for example, automatically fetch area data and combine the area data with crop data; the latter can even be determined automatically. Result signals obtained from multiple measurements can be stored locally or uploaded to a remote location using a communications network, e.g., via a wireless network connection. The result signals can also include data related to at least one benign organism.

[0094] According to another aspect, the method also includes: · Determining a sampling method for collecting environmental samples in response to the crop data, the area data, and any of the selected sensor configurations.

[0095] The determination of the sampling method can be performed by the processing means using the same database used to select the sensor configuration, or it can be a separate database. According to one embodiment, information related to the sampling method is output via an HMI. This can have the advantage that on-site analysis can be performed by non-expert users. For example, when measuring on a farm, farmers do not need to know in advance how to perform sampling. Thus, the portable device can guide the user through the correct method of collecting the sample. There are different types of sampling methods that vary significantly based on the type of crop, organism, sensor configuration, or a combination thereof. Sampling can involve either collecting a specific part of the leaf or another part of the crop, extracting the sample through a hole drilled in the leaf, collecting all or part of the organism, etc. Therefore, if sampling is not performed properly, the detection of the organism may be unreliable. In some cases, the required sampling or sample preparation may be so specialized that specialized knowledge is required, e.g., NaOH-based extraction and Whatman FDA cards. Therefore, the proposed teachings, when combined with the remaining embodiments, also facilitate the process for the user by guiding the user through the required sampling method and process. The user may be guided through the process, for example, through the sample collection process and / or the sample preparation process, thus avoiding the sampling having to be performed by an expert user or by the requirement of prior knowledge of any of such processes.

[0096] According to yet another embodiment, the sampling and / or sample preparation is performed automatically by a portable device, for example in the form of a robot, which is capable of automatically performing the sampling according to the sampling method selected by the processing means.

[0097] According to one embodiment, the method also includes: · determining a treatment method for controlling at least one of the at least one relevant pest present at the crop site in response to the result signal or the processed result signal; Optionally, providing information about treatment methods as recommendations to the user.

[0098] It will therefore be understood that the treatment method is determined by the processing means by analyzing the result signal or the processed result signal. The treatment method determination can be performed by the processing means either using the same database as used to select the sensor configuration or using a separate database. The database can contain additional information regarding the efficacy of different agricultural products against a given pest. For example, the database can contain information regarding appropriate pesticides for combating pests that are resistant to a particular or several pesticides. Such information can be updated periodically, for example, as resistance changes in type, significance, and local spread.

[0099] The treatment recommended to the user may also be accompanied by a product recommendation. These product recommendations may be selected by economic effectiveness, biological effectiveness, availability, etc. The recommended product may be automatically ordered from a vendor. For example, the method may include the availability of stocked recommended products at the farm or at an organizational unit belonging to the farm where the crop plant is growing or intended to grow. The information may be stored in a database. Thus, the method includes receiving information from the database regarding the local availability of the recommended treatment, i.e., the availability of the recommended product. If the data indicates that product inventory is low (e.g., less than 10% of capacity) or empty, the method may include the further step of automatically ordering the product from the vendor. Of course, the ordering may alternatively be performed by the user receiving information regarding the recommended treatment.

[0100] According to one embodiment, treatment recommendation information is output via an HMI. This has the advantage that on-site analysis can be performed by a non-expert user and recommendations for treating any relevant pests can be provided to the user on the spot. The treatment method may be determined in response to multiple result signals or processed result signals. The multiple such signals may be either previous and recent results from the same or nearby sites, or the multiple signals may be a combination of signals related to adjacent sites, or a combination of previous signals and signals from adjacent sites.

[0101] According to another aspect, if a field map is generated, the method further includes: determining a treatment method for controlling at least one of the one or more pests present at the crop site by analyzing the field map via the treatment means;

[0102] It will be appreciated that by doing so, the treatment means can recognize which areas of the crop require what type of treatment, and therefore future treatments can be optimized according to the spread of the organisms over the crop area or parts thereof. Another combined advantage is that treatments can be selected according to the density of the spread of the organisms, and therefore problem areas of the crop can be treated appropriately.

[0103] According to another aspect, the method also includes: conducting a treatment according to the treatment method to control at least one of the at least one relevant pest detected in the crop locus;

[0104] The treatment can be performed using the portable electronic device, or can be performed using a separate treatment device operatively coupled to the portable electronic device.

[0105] It is understood that the treatment may be a spraying program that includes spraying one or more active ingredients to control at least one of one or more pests. For example, the treatment may include spraying a fungicide on at least a portion of the crop or crop site to control fungi detected by the treatment means. The active ingredient may be a chemical compound and / or it may be a biological compound used, for example, for biocontrol. In some cases, the treatment may also include physical removal of organisms from the crop or crop site.

[0106] According to one embodiment, at least a portion of the result signal is also fed back to the database to further improve the database. Portions of the result signal may include the organism detected and its genotype. This portion may also include a treatment recommendation determined and / or applied to control the organism. From subsequent measurements performed at or near the same site, or along the same or similar locations, the processing means can determine whether the treatment was effective in controlling the harmful organism. This can be useful for identifying new, previously undetected resistance mutants.

[0107] Similarly, an electronic device may be provided that includes processing means configured to perform the steps disclosed herein.

[0108] More specifically, similar to the first aspect, there may be provided an electronic device comprising a processing device configured to receive: · Crop data indicating the type of crop; area data indicative of at least one pest present in or around said crop; The processing means is configured to select a sensor configuration from a database, the sensor configuration being suitable for selectively genotyping at least one relevant pest, the at least one relevant pest being among the at least one pest, and the selection being performed in response to the crop data and the area data.

[0109] According to one aspect, the electronic device further comprises a human machine interface ("HMI") operatively coupled to the processing means. The HMI is arranged to output information regarding the appropriate sensor configuration. The information is output to a user.

[0110] According to another aspect, the sensor arrangement is configured to perform in situ genotyping of at least one or more relevant pests from an environmental sample obtained from the crop field; the sensor arrangement is operatively connected at a sensor interface, and the electronic device is further configured to generate a result signal at the sensor interface via the sensor arrangement.

[0111] According to a further aspect, the processing means is configured to process the result signal to determine whether at least one or more relevant pests are present in the environmental sample, thereby detecting the presence of any of the at least one or more relevant pests on the crop site via the electronic device.

[0112] The electronic device is preferably a portable device or a portable electronic device.

[0113] More specifically, a portable electronic device may be provided comprising a processing means and a sensor interface, the processing means being configured to: · Receive crop data indicating the type of crop; ·receive area data indicating at least one pest that may be present at or near the location of the crop; · selecting from the database a sensor configuration suitable for selectively genotyping at least one relevant pest, the at least one relevant pest being among the at least one pest, the selection being performed depending on the crop data and the area data; The sensor arrangement is configured to perform in situ genotyping of at least one relevant pest from an environmental sample obtained from the crop site; the sensor arrangement is operatively connected at a sensor interface, and the electronic device is further configured at the sensor interface to generate a result signal via the sensor arrangement, and the processing means is configured to process the result signal to determine whether the at least one relevant pest is present in the environmental sample, thereby detecting the presence of any of the at least one relevant pest on the crop site via the electronic device.

[0114] According to another embodiment, the electronic device is further configured to process the result signal via the processing means to further determine whether at least one benign organism is present in the environmental sample.

[0115] According to another aspect, the electronic device also comprises a geographic location module for determining the geographic location of the crop field. According to another aspect, the electronic device also comprises a communication module operatively connected to the processing means. The communication module can be used to connect to a communication network and / or a database. For example, the communication module can be a wireless network connection module, and / or a Bluetooth module or the like.

[0116] According to another aspect, an electronic device is configured as follows: · using the processing means to generate field result data by combining the processed result signals with location data of the crop sites where the environmental samples were collected; ·Storing field results data in at least one database; · Combining data from multiple field results data to obtain a field map representing the territorial extent of any one of at least one relevant pest.

[0117] According to another aspect, the electronic device is further configured as follows: · By analyzing the field map via the processing means, determining at least one site where another measurement or detection is required.

[0118] According to another aspect, the electronic device is further configured as follows: Determine a sampling method for collecting environmental samples in response to crop data, area data, and any selected sensor configurations.

[0119] According to one embodiment, the electronic device is further configured as follows: · determining a treatment method for controlling at least one of the at least one relevant pest detected at the crop site in response to the result signal or the processed result signal; Optionally, provide users with information about treatment options as recommendations.

[0120] According to one embodiment, the electronic device is further configured as follows: a) receiving information from a database about pesticides that are effective in combating at least one pesticide-related pest; b) generating a recommendation, such as a product recommendation, for the user regarding a recommended treatment method based on the information a) stored in the database and the result signal or the processed result signal;

[0121] According to one embodiment, the electronic device is further configured as follows: c) receive information from the database about the local availability of recommended treatments; d) Non-existence or low availability will impact the delivery of recommended products from vendors.

[0122] According to another aspect, the electronic device is further configured as follows: determining a treatment method for controlling at least one of the at least one relevant pest detected on the crop site by analyzing the field map via the treatment means;

[0123] According to another aspect, the electronic device is further configured as follows: · Carry out treatment according to the prescribed treatment regimen.

[0124] The treatment is carried out at least in part at the crop site.

[0125] The processing means may be a general-purpose processing device such as a microprocessor, microcontroller, central processing unit, etc. More specifically, the processing means may be a processor implementing a CISC (Complex Instruction Set Computing) microprocessor, a RISC (Reduced Instruction Set Computing) microprocessor, a VLIW (Very Long Instruction Word) microprocessor, or other instruction set or processor implementing a combination of instruction sets. The processing means may also be one or more special-purpose processing devices such as an ASIC (Application-Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), a CPLD (Complex Programmable Logic Device), a DSP (Digital Signal Processor), a network processor, etc. The methods, systems, and devices described herein may be implemented as software in a DSP, microcontroller, or any other processor, or as hardware circuitry within an ASIC, CPLD, or FPGA. It should be understood that the term “processing means” or processor can also refer to one or more processing devices, such as a distributed system of processing devices located across multiple computer systems (e.g., cloud computing), and is not limited to a single device unless otherwise specified.

[0126] Viewed from another perspective, a computer program may be provided for an electronic device, comprising instructions which cause the electronic device to perform the steps disclosed herein when the program is executed by a processing means of the electronic device.

[0127] For example, a computer program may be provided which includes instructions which, when executed by a processing means of an electronic device, cause the electronic device to: · receiving, at the processing means, crop data indicative of a type of crop; · receiving, at the processing means, area data indicative of at least one pest that may be present at or near the location of the crop; selecting, via said processing means, from a database a sensor configuration suitable for selectively genotyping said at least one relevant pest, said at least one relevant pest being among said at least one pest, said selection being performed in response to said crop data and said area data.

[0128] As a further example, a computer program may be provided comprising instructions that, when executed by a processing means of an electronic device comprising a sensor interface, cause the electronic device to: · at the processing means, receiving crop data, the crop data indicating a type of crop; · receiving area data at a processing means; where the area data indicates the presence of one or more pests in an area in which the crop site is located; · using the processing means to select from the database a sensor configuration suitable for selectively genotyping at least one relevant pest, the at least one relevant pest being among the one or more pests, the selection being performed in response to the crop data and the area data; · determining the genotype of at least the relevant pest by analyzing environmental samples obtained from the crop field using in situ a sensor configuration connected to the sensor interface and generating a result signal via the sensor configuration at the sensor interface; using the processing means to process the result signal to determine whether at least one associated pest is present in the environmental sample, thereby detecting the presence of any of the at least one associated pest on the crop site via the portable device.

[0129] Viewed from yet another perspective, a computer readable data carrier may be provided having stored thereon the computer program disclosed herein, and thus a non-transitory computer readable medium may also be provided that stores a program for causing a suitable electronic device to perform any of the method steps disclosed herein.

[0130] A computer-readable data carrier includes any suitable data storage device on which is stored one or more sets of instructions (e.g., software) that implement any one or more of the methods or functions described herein. The instructions may also reside, completely or at least partially, in main memory and / or within the processor during execution of the instructions by the computer system, main memory, and processing device, which may constitute a computer-readable storage medium. The instructions may also be transmitted or received over a network via a network interface device.

[0131] A computer program for implementing one or more of the embodiments described herein may be stored and / or distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems. However, the computer program may also be presented over a network such as the World Wide Web and downloaded into the working memory of a data processor from such a network.

[0132] Viewed from another point of view, a data carrier or data storage medium for making a computer program element downloadable can also be provided, this computer program element being arranged to perform a method according to one of the previously described embodiments.

[0133] The word "comprise" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or controller or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims are not to be interpreted as limiting the scope. [Brief explanation of the drawings]

[0134] Hereinafter, embodiments will be described with reference to the accompanying drawings. [Figure 1] 1 shows a block diagram representation of a portable electronic device deployed in a crop field. [Figure 2] 1 shows a spectral sketch to illustrate the target approach of detection. [Figure 3] 1 shows a flow chart of a sensor configuration selection method. [Figure 4] 1 shows a flow chart of selective genotyping performed after selection. DETAILED DESCRIPTION OF THE INVENTION

[0135] FIG. 1 shows a block diagram 100 illustrating a scenario in which some of the present teachings may be applied. A geographical section 101 is shown, representing a geographical area of ​​land, in a non-limiting sense. The section 101 is planted with a crop, including a plant 103. The crop, i.e., the plant 103, is planted in a field 102, a geographical area defined by boundaries 102a-d. A left side 102d of the field 102 is shown planted with another crop, including another plant 104, different from the plant 103 on the field 102. A detection system 105 according to one embodiment of the present teachings is shown disposed at a site 106a on the field 102. A zoomed-in view 106b of the site 106a is also shown, showing an example of the detection system 105 in more detail. The detection system 105 includes an electronic device 109, shown as a handheld device. The detection system 105 also includes a plurality of sensors or sensor configurations 112, which may be stored, for example, in a kit 110. Any of the sensors 112, for example a selected sensor or sensor configuration 113, can be connected to the electronic device 109 via a sensor interface. The sensor interface can be wireless or wired. The electronic device 109 includes a human machine interface ("HMI"), shown here as including a stylus 114 and a display 111 for a user to interact with the device 109. The HMI may also include audio devices such as loudspeakers. The electronic device 109 also comprises a processing means or computer processor.

[0136] The sensing device 105, or more specifically, the electronic device 109, may include geographic location means or a geographic location module for receiving location information. The location data represents the geographic location of the site 106a. For example, the device 109 may receive the location data via one or more satellites 118. Geolocation systems such as GPS and GALILIO are available as modules that can be integrated into the device 109 and / or the sensors 112, 113, for example. Alternatively, or additionally, the location data may be obtained from one or more cellular networks 109. Alternatively, or additionally, the location data may be obtained from one or more radio stations or beacons 120a-b. The system 105, or preferably the electronic device 109, may be connected to at least one network, for example, via a data connection 129 with the cellular network 119 and / or via a wireless connection with one of the radio stations 120a and / or 120b.

[0137] The system 105 or device 109 also has a functional connection 125 to at least one database 115. In some cases, the database 115 may be at least partially contained within a local database, i.e., within the memory of the system 115, or more specifically, within the memory of the device 109. In such cases, the database connection is at least partially internal to the system 105 or device 106. In some cases, the database 115 may be at least partially a remote database, i.e., located elsewhere relative to the system 105 location 106a. In such cases, the database connection 125 is at least partially external to the system 105. Thus, the database connection 125 may be established via a cellular network 119 and / or one or more wireless stations 102a, b. At least one database 115 may be at least partially located on a cloud service 116. In some cases, the external database connection 125 may be located remotely from the test location 106a. This may be relevant, for example, when a cellular network or wireless connection is not available from the test site. In such cases, database 115 may be an internal database that can be synchronized with a remote database when a network connection is available. Synchronization may be performed directly via cellular connection 129 and / or wireless network 120, if available, or synchronization may be performed indirectly via connection 127 to server 117. In some cases, server connection 127 is a wired connection between device 109 and server 117, but in other cases may be a wireless connection, for example, via data connection 129 or radio 120. In some cases, remote database 115 may reside at least partially on server 117, or server 117 may also provide access to a remote database 115 that resides elsewhere.

[0138] When the device 105 is deployed at a crop field, e.g., field 106a, the processing means receives crop data indicating the type of crop, i.e., the type of plant 103. The crop data may be entered by a user, e.g., using an HMI, and / or may be acquired automatically via a crop sensor and / or via previous measurements. The crop sensor may, for example, be part of an image recognition system. The processing means also receives area data indicating at least one pest that may be present at or near the location of the crop 103. To this end, the area data may provide information about which organisms are or have been present within a certain distance of the field 102. In some cases, the area data may be acquired at least in part from the server 117, the database 115, the cellular network 119, and / or the wireless network 120. The area data may include location data. In some cases, the area data may be available from one or more public databases or internet services. The area data may also include weather data, e.g., wind strength and direction 131.

[0139] If the infected area 140 is located between the path of the wind 131 upstream of the field 102, there is a high probability that harmful organisms from the infected plants 130 have been transported to at least part of the crop 103. Therefore, the specific distance of the field 102 to be evaluated for the relevance of the detection of the pest is adapted according to the prevailing conditions such as the wind 131 or its history.

[0140] In some cases, other crops 104 may have been infested with a different pest. In such cases, the area data may also include information related to the different pest. However, the different pest may not infect the crop plants 103. Therefore, it may not be useful to treat the crop 103 for a different pest that only affects other crops 104, or more specifically, does not affect the crop 103. Such an organism may be considered a pest that is irrelevant to the crop 103.

[0141] Similarly, other organisms may be present that are harmful to the crop 103, but such organisms have not been detected within a certain distance of the field 102, or even within the site 106a. Therefore, it may not make sense to detect such additional organisms. Such organisms may also be considered as irrelevant pests of the crop 103.

[0142] According to the present teachings, by combining crop data with area data, the processing means can automatically determine a pest of interest or at least one related pest when both the crop data and the area data indicate the same pest. It will be understood that if at least one pest indicated by the area data does not correspond to an organism that can affect the crop, none of the at least one pest is a related pest, and therefore, detection does not need to be performed. Similarly, if all of the at least one pest indicated by the area data correspond to respective organisms that can affect the crop, all of the at least one pest are related pests and therefore recommended for detection. Therefore, if at least one related pest is indicated by combining the crop data and the area data, the processing means can automatically select a sensor configuration including one or more sensors, each of which is targeted to detect a specific individual organism of interest that is different from the organisms that other sensors in the sensor configuration can detect. This makes it possible to determine a sensor configuration optimized for detecting the organism of interest or related organisms. A user does not need to be an expert on organisms, crops, or their detection.

[0143] When the selected sensor configuration 113 is used to perform detection, the device 109 can guide the user through the sampling process via the HMI. The sampling process may vary depending on the crop and / or associated organisms being detected. The sampling process may depend on the selected sensor configuration 113. Thus, the user need not be an expert or biologist. It will also be understood that the above-described method is suitable for implementation as an autonomous process, for example, the system 105 may be a robotic system that automatically selects the sensor configuration 113, then interfaces the configuration 113 to a processing unit, acquires one or more environmental samples from the site 106a, selectively genotypes the environmental samples, generates result signals via the sensor configuration, and processes the result signals using the processing means to determine whether one or more pests are present in the environmental samples acquired from the crop site 106a. The robotic system can then select another site for detection at another site. Thus, the present teachings are suitable for both detection performed by a portable device operated by a user or by an at least partially autonomous process.

[0144] In response to a positive detection of a pest at a site, the treatment means determines an appropriate treatment for controlling the pest. If two or more related pests are detected at a site, the treatment means can determine an optimized treatment for treating two or more pests on the site. This helps minimize the amount and number of active ingredients required to control the organisms, rather than treating each pest separately. Such treatment optimization can be a significant advantage even for inexperienced users. Therefore, it can have cost and environmental benefits. A treatment can be, for example, a spray program that involves spraying a specific active ingredient or a mixture thereof on infected plants. Thus, the treatment means can select an appropriate treatment depending on the crop and organism. Furthermore, the treatment means can even select an appropriate treatment optimized depending on the weather and / or season. The system 105 or device 109 can also guide the user through the treatment process, which can include the name of an appropriate treatment product, the start time of the treatment using the product, the input amount, application amount, number of product applications, or a combination of treatment products depending on one or more result signals from the site or adjacent sites. Rather than risking applying too much or too little product, the user can be guided through an optimized application according to outcome data from one or more outcome signals. Again, it should be understood that an autonomous treatment system could be used that does not guide the user but instead operates in response to outcome data, and even location data, similar to that outlined above. The processing system could be the same system 105 or a separate system, such as at least one drone 166. The drone 166 could also be equipped with location sensing means, such as a geolocation module that acquires location signals 138 from one or more satellites 118. The drone could also acquire location data via the detection system 105 or via the cellular network 119 or wireless network 120, as described above with respect to system 105.

[0145] FIG. 1 also shows an infected portion 150 of the crop plant 103. The portion 150 may have been infected by a pathogen carried by wind 131 from the infected region 140. Such a portion 150 can be determined / detected, for example, by combining result signals from multiple crop sites. Further combining the result signals with location data for each site can result in a map depicting the geographic distribution of one or more relevant organisms. The infected portion 150 may also be referred to as a hot zone for the organisms present in the portion 150. Such a map is useful for targeted treatment of infected plants within and possibly surrounding the hot zone 150. Subsequent detection can be performed at the same or similar sites in the field 102 to track the effectiveness of treatment and / or spread, thereby optimizing further treatment. This also has cost and ecological benefits by avoiding treatment in response to controlled detection of harmful organisms. These benefits are synergistically related by enabling targeted detection to be performed in a rapid manner, allowing frequent and high-density measurements.

[0146] In some cases, the system 105 automatically arranges the sequence of one or more sensors in the selected sensor configuration if one or more sensors are not available in the system 105 or are expected to be depleted for future planned detections. In some cases, the system 105 can automatically order treatment products as needed to control one or more relevant pests that are detected.

[0147] FIG. 2 shows a symbolic representation 200 of the targeting approach for selection and organism detection proposed in the present teachings. Across the entire spectrum of organism genomes 250, different organisms may exist at different locations on the spectrum. The organism spectrum may include organisms that can affect the crop 103, such as a first group of organisms 201 and a second group of organisms 202. Additionally, there may be a third group of organisms 203 that cannot affect the crop 103. If the area data indicates that organisms 201 and 203 may be present at or near the location of the crop 103 or field 102, the processing means can combine the crop data with the area data, thereby automatically excluding organism 203 from the detection range. As a result, the processing means selects only the first group of organisms 201 for detection, thus defining them as relevant pests. Therefore, organisms 202 of the second group are also excluded from the detection range as irrelevant organisms, since these organisms are not present at or near the location of the crop. Thus, the processing means is responsive to the crop data and area data to select from the database 115 a sensor configuration 113 suitable for selectively genotyping the relevant pests 201a-d. It will be understood that the term group may refer to a single organism and not necessarily a group of organisms. Thus, each specific organism may be checked for relevance by the processing means to define a range of detection. Range of detection refers to the organisms selected for detection by the processing means by combining the crop data and area data. Thus, the sensor configuration 113 is selected such that it can selectively genotype these selected organisms.

[0148] FIG. 3 shows a flowchart 300 for selecting a sensor configuration. In a first step 301, crop data is received by the processing means. The crop data indicates the type of crop. For example, the crop data indicates that the crop plant 103 is wheat. The crop data can further specify the type of crop. For example, the crop plant 103 indicates that the crop plant 103 is a durum wheat type. In a next step 302, area data is received by the processing unit. The area data indicates at least one pest that may be present at or around the crop location 106a. In a next step 303, the processing means selects a sensor configuration 113 from the database 115 depending on the crop data and the area data. The sensor configuration is suitable for selectively genotyping at least one relevant pest. Of course, the sequence of the first step 301 and the subsequent step 302 may be interchanged. The processing means can still select the sensor configuration 113 from the database 115 by combining the crop data and the area data. In an optional subsequent step 304, information about the selected sensor configuration 113 is output via an HMI, such as a graphic display.

[0149] The flowchart 300 can be implemented on a suitable electronic device 109 comprising processing means and a sensor interface for operatively connecting the sensor arrangement 113 to the processing device, thereby realizing the detection system 105.

[0150] FIG. 4 illustrates another flowchart 400 that can, for example, follow steps 301-303 of the first flowchart 300. Step 304 may optionally be present. In a first step 401, selective genotyping of an environmental sample is performed using the sensor configuration 113 operatively connected at the sensor interface. The environmental sample is obtained from the crop site 106a. A result signal is generated at the sensor interface by performing selective genotyping via the sensor configuration 113. In a further step 402, the result signal is processed by the processing unit to determine whether one or more relevant pests are present in the environmental sample. Thus, the electronic device 109 can detect whether any of the one or more relevant pests are present on the crop site 106a. As an optional further step 403, the processing means also determines from the result signal whether at least one benign organism is present in the environmental sample. This determination is in the form of a processed result signal generated by the processing means, which is indicative of the presence of one or more organisms in the environmental sample. As a further optional step, with or without step 403, the processing means generates field result data by combining the processed result signals with location data of the crop sites 106a where the environmental samples were collected. Also, optionally, the processed result signals collected from detections performed on multiple sites can be used to generate a map representing the spread of any of one or more relevant organisms over the geographic area covered by the multiple sites. In a further optional step, a decision can be made for appropriate sampling steps, processing, and further measurements. Optionally, a user can be guided through each or all of the steps, or further optionally, the steps can be performed in an autonomous manner.

[0151] Various examples of methods suitable for on-site detection, electronic devices for on-site detection, and computer software products that perform any of the associated method steps disclosed herein are disclosed above. However, those skilled in the art will understand that changes and modifications can be made to these examples without departing from the spirit and scope of the appended claims and their equivalents. Furthermore, it will be understood that aspects from the method and product embodiments described herein can be freely combined.

Claims

1. 1. A computer-implemented method comprising: - receiving, in a processing means, crop data indicative of a type of crop; - receiving, at said processing means, area data indicative of at least one pest that may be present at or around said crop location; - using said processing means to select from a database a sensor configuration suitable for selectively genotyping at least one relevant pest, said at least one relevant pest being among said at least one pest, said selection being performed in response to said crop data and said area data; A method having the following.

2. The method of claim 1 , wherein the sensor configuration is suitable for in situ genotyping of the at least one relevant pest.

3. 3. The method of claim 1, wherein the method is applied to detect DNA sequences indicative of insecticide resistance, and the sensor arrangement is configured to detect the DNA sequences indicative of the insecticide resistance.

4. 4. The method according to claim 1, further comprising the step of outputting information relating to the suitable sensor configuration via a human machine interface operatively coupled to the processing means.

5. The method comprises: performing genotyping of the at least one relevant pest from an environmental sample obtained from a crop site using the sensor arrangement connected at a sensor interface in the field and generating a result signal via the sensor arrangement at the sensor interface; using said processing means to process said result signal to determine whether one or more of said at least one relevant pest is present in said environmental sample; further comprising 5. The method according to any one of claims 1 to 4.

6. 6. The method of claim 5, further comprising the step of determining whether at least one benign organism is present in the environmental sample by processing the result signal using the processing means.

7. The method comprises: generating field result data using said processing means by combining said processed result signals with location data of said crop sites where said environmental samples were collected; storing the field results data in at least one database; combining data from a plurality of said field result data to obtain a field map representing the areal extent of any of said one or more associated organisms; having 7. The method according to claim 5 or 6.

8. 8. The method of claim 7, further comprising the step of determining, by analyzing the field map via the processing means, at least one site where further measurement or detection is required.

9. the method comprising using the processing means to determine a sampling method for collecting the environmental sample in response to any of the crop data, the area data, and the selected sensor configuration; 9. The method according to any one of claims 5 to 8.

10. the method comprising determining a treatment regimen for controlling at least one of the at least one associated pest present at the crop site in response to the result signal or the processed result signal.

10. The method according to any one of claims 5 to 9.

11. the method comprising determining a treatment regime for controlling at least one or more pests present at the crop site by analyzing the field map via the processing means; 9. The method according to claim 7 or 8.

12. 12. The method of claim 11, further comprising using the processing means to provide information about the treatment method as a recommendation to a user.

13. the method comprising administering a treatment according to the treatment method to control at least one of the at least one relevant pest detected at the crop site; 13. The method according to claim 11 or 12.

14. An electronic device configured to perform the method of any one of claims 1 to 13, said electronic device comprising said processing means.

15. A computer program comprising instructions which, when executed by the processing means of an electronic device, cause the electronic device to carry out a method according to any one of claims 1 to 13.

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