System and method for simulating crop yield based on detection of plant stressors in crops

Abstract

One variation of a method includes: acquiring an image of a sensitive plant in a crop of a crop type seeded within a geographic area, and the sensitive plant configured to signal the presence of a stressor; interpreting a first pressure of the stressor within the first sub-region based on a first subset of features extracted from the image; interpreting a second pressure of the stressor in a second sub-region based on a second subset of features extracted from the image; deriving a pressure map representing pressures of stressors within the geographic area based on the first pressure and the second pressure; obtaining a yield model that correlates the stress of the stressor with the crop yield of the crop of the crop type in the geographic area; and predicting a crop yield of the crop of the crop type in the geographic area based on the pressure map and a yield model.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 417,247 filed on October 18, 2022 and U.S. Provisional Application No. 63 / 338,618 filed on May 5, 2022, each of which is incorporated by reference in its entirety.

[0002] This application is related to U.S. patent application No. 17 / 592,275 filed on February 3, 2022, which is a continuation of U.S. patent application No. 17 / 217,840 filed on March 30, 2021, which is a continuation of U.S. patent application No. 16 / 908,526 filed on June 22, 2020, which claims the benefit of U.S. Provisional Application No. 62 / 864,401 filed on June 20, 2019, each of which is incorporated by reference in its entirety. Technical Field

[0003] The present invention relates generally to the field of agriculture, and more particularly to new and useful methods for detecting stressors in crops and modeling crop yield in the field of agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 is a flow chart representation of a method; Figure 2A and Figure 2B is a flowchart representation of a variation of the method; Figure 3 is a flowchart representation of a variation of the method; Figure 4A and Figure 4B is a flowchart representation of a variation of the method; and Figure 5 is a flowchart representation of a variation of the method. Description of Embodiments

[0005] The following description of the embodiments of the present invention is not intended to limit the present invention to these embodiments, but to enable those skilled in the art to make and use the present invention. The variations, configurations, implementations, example implementations, and examples described herein are optional and are not limited to the variations, configurations, implementations, example implementations, and examples they describe. The present invention described herein may include any and all combinations of these variations, configurations, implementations, example implementations, and examples.

[0006] 1. Methods like Figure 1 , Figure 2A , Figure 2B , Figure 3 , Figure 4A , Figure 4B and Figure 5 As shown, the method S100 includes: in block S110, acquiring an image feed of sensitive plants of a sensitive plant type sown in a group of agricultural fields within a geographic area, the image feed being recorded during a first time period, the sensitive plants of the sensitive plant type being configured to signal the presence of a group of stressors at the sensitive plants; in block S112, interpreting a first stress of a first stressor in the first sub-area of ​​the group of stressors based on features extracted from a first image subset of the sensitive plants depicted in a first sub-area within the geographic area in the image feed; in block S112, interpreting a first stress of a first stressor in the first sub-area of ​​the group of stressors based on features extracted from a first image subset of the sensitive plants depicted in a first sub-area within the geographic area in the image feed; Features extracted from a second subset of images of sensitive plants explain a second pressure of the first stressor in the second sub-area; in box S120, a first pressure map of the geographic area is derived based on the first pressure and the second pressure, the first pressure map indicating the presence of the set of stressors in the geographic area during a first time period; in box S122, a yield model is obtained, which associates the pressure of the set of stressors in the geographic area with the crop yield of crops of a set of crop types in the target area; and in box S130, a first crop yield of a crop of a first crop type in the set of crop types in the geographic area is predicted based on the first pressure map and the yield model.

[0007] A variation of method S100 includes: in block S110, acquiring an image feed of a population of sensitive plants of a crop type sown in an agricultural field, the image feed being recorded by an aerial sensor during a first time period, the population of sensitive plants being configured to signal the presence of a set of conditions and including a first group of sensitive plants arranged in a first area of ​​the agricultural field; in block S112, interpreting the first set of conditions at plants in the first area based on features extracted from a first subset of images in the image feed depicting the sensitive plants in the first area; in block S116, acquiring a first set of conditions for the plants in the first area of ​​the agricultural field; a set of target conditions defined for plants; in box S140, predicting a first health score for plants in a first area based on a first difference between the first set of conditions and the set of target conditions; in box S150, selecting a first treatment path from a set of treatment paths for the plants in the first area based on the first health score, the first treatment path being configured to drive conditions of the plants in the farmland toward the set of target conditions; in box S160, generating a prompt to implement the first treatment path during a second time period after the first time period; and in box S162, sending the prompt to a user associated with the farmland.

[0008] A variation of method S100 includes: in block S110, acquiring an image feed of a sensitive plant population in a crop of a crop type sown in a target area, and the sensitive plant population is configured to signal the presence of a set of stressors at the sensitive plants, the image feed being captured by an optical sensor during a first time period; in block S112, interpreting a first time series of stress data for plants in a first sub-area of ​​a set of sub-areas of the target area based on features extracted from a first subset of images in the image feed captured during the first time period, the first time series of stress data representing changes in stress of the set of stressors at the plants in the first sub-area during the first time period; in block S114, interpreting a first time series of stress data for plants in a first sub-area of ​​a set of sub-areas of the target area based on features extracted from a first subset of images in the image feed captured during the first time period, the first time series of stress data representing changes in stress of the set of stressors at the plants in the first sub-area during the first time period; features extracted from a second subset of images in the image feed, explaining a second time series of stress data for plants in a second sub-region of the set of sub-regions in the target area, the second time series of stress data representing changes in stress of the set of stressors at plants in the second sub-region during the first time period; in box S122, obtaining a yield model that associates the stress of the set of stressors in the target area with the crop yield of crops of a set of crop types in the target area; and in box S130, predicting a first crop yield of a first crop type in the set of crop types in the target area during a target harvest time period after the first time period based on the first time series of pressure data, the second time series of pressure data, and the yield model.

[0009] A variation of method S100 includes: in block S110, acquiring an image feed of a sensitive plant population in a crop of a crop type sown in a target area, and the sensitive plant population is configured to signal the presence of a set of stressors at the sensitive plants, the image feed being captured by an optical sensor during a first time period; in block S112, interpreting a first stress of a first stressor in the set of stressors in a first sub-area within the target area based on features extracted from a first subset of images depicting a first set of sensitive plants in the sensitive plant population in the image feed; in block S114, interpreting a first stress of a first stressor in the set of stressors based on features extracted from a first subset of images depicting a first set of sensitive plants in the sensitive plant population in the image feed; Features extracted from a second subset of images of a second group of sensitive plants in the sensitive plant population explain a second pressure of the first stressor in a second sub-area within the target area; in box S120, a first pressure map of the target area during a first time period is derived based on the first pressure and the second pressure; in box S122, a yield model is obtained, which associates the pressure of the first stressor in the target area with the crop yield of the crop of the crop type; and in box S130, based on the first pressure map and the yield model, a first crop yield of the crop of the crop type in the target area during a target harvest time period after the first time period is predicted.

[0010] A variation of method S100 includes: in box S110, obtaining an image feed of a sensitive plant population of a crop type (e.g., cotton, soybeans, corn) sown in a farmland recorded by an aerial sensor during a first time period, the sensitive plant population being configured to signal the existence of a set of conditions, the sensitive plant population including a first group of sensitive plants arranged in a first sub-area of ​​the farmland and a second group of sensitive plants arranged in a second sub-area of ​​the farmland; in box S112, interpreting a first set of conditions at plants in the first sub-area based on a first set of plant signals (e.g., fluorescence signals) detected in a first image subset of sensitive plants depicting the first sub-area in the image feed; and in box S114, interpreting a second set of conditions at plants in the second sub-area based on a second set of signals (e.g., fluorescence signals) detected in a second image subset of sensitive plants depicting the second sub-area in the image feed. Method S100 also includes: in box S116, obtaining a first set of target conditions defined for plants in a first sub-area; in box S140, predicting a first health score for plants in a first sub-area of ​​the farmland based on a first difference between the first set of conditions and the first set of target conditions; and in box S150, selecting a first treatment path for plants in the first sub-area based on the first health score.

[0011] In the previous variation, method S100 further includes: in box S116, obtaining a second set of target conditions defined for plants in the second sub-region; in box S140, predicting a second health score for plants in the second sub-region of the farmland based on a second difference between the second set of conditions and the second set of target conditions; and in box S150, selecting a second treatment path for plants in the second sub-region based on the second health score. In one variation, method S100 further includes: in box S170, assembling a treatment map based on the first treatment path selected for the first sub-region and the second treatment path selected for the second sub-region, specifying the location and amount (e.g., frequency and / or amount) of application for each treatment path selected for the crop.

[0012] A variation of method S100 includes: in block S110, acquiring an image feed of a population of sensitive plants sown in a crop of a crop type (e.g., cotton, soybean, corn) within a target area (e.g., a target geographic area), the image feed recorded by an aerial sensor during a first time period, the population of sensitive plants configured to signal the presence of a set of stressors (e.g., abiotic stressors and / or biotic stressors) at the sensitive plants; in block S112, interpreting a first stress of a first stressor of the set of stressors in a first sub-area within the target area based on a first signal (e.g., a fluorescent signal) detected in a first subset of images depicting sensitive plants in the first sub-area in the image feed; In box S114, based on a second signal (e.g., a fluorescent signal) detected in a second image subset depicting sensitive plants in a second sub-area within the target area in the image feed, a second pressure of the first stressor in the second sub-area is interpreted; in box S120, based on the first pressure and the second pressure, a first pressure map of the target area during a first time period is derived; in box S122, a yield model is obtained that associates the pressure of the first stressor in the target area with the crop yield of the crop of the crop type; and in box S130, based on the first pressure map and the yield model, a first crop yield of the crop of the crop type in the target area during a target harvest time period after the first time period is predicted.

[0013] In one variation, method S100 further includes: acquiring a pressure model configured to predict a future pressure of a first stressor in the target area based on a pressure of the first stressor detected in the target area; and in box S180, predicting a second pressure map of the target area during a second time period after the first time period based on the first pressure map and the pressure model. In response to the second pressure map predicting that a third pressure of the first stressor in a third sub-area within the target area exceeds a threshold pressure, method S100 also includes: in boxes S160 and S162, sending a first prompt to a first group of users associated with maintenance of crops located within the third sub-area to implement a first mitigation technique from a set of mitigation techniques, the first mitigation technique being configured to mitigate the pressure of the first stressor; in boxes S160 and S162, sending a second prompt to a second group of users associated with the manufacture of crop treatments to generate a supply of a first crop treatment corresponding to the first mitigation technique and configured to treat the pressure of the first stressor; and in boxes S160 and S162, notifying a third group of users associated with maintenance of crops near the third sub-area of ​​the prediction of a third pressure of the first stressor in the third sub-area during a second time period.

[0014] 2. Application Typically, the boxes of method S100 can be executed by a computer system (e.g., a remote server, a local computing device, a computer network) in collaboration with a sensitive plant platform (e.g., a local application or a web-based application) to: detect signals generated by sensitive plants; interpret (e.g., estimate) stressors in crops, on multiple crops, and / or at sensitive plants sown in a particular geographic area based on these sensitive plant signals; derive a set of crop models for a particular crop and / or for a particular crop area, the set of crop models being configured to predict various indicators of crop health and / or crop operation, such as the presence of stressors in crops, crop yield (e.g., for a particular growing season), and / or crop resistance (e.g., the speed and / or magnitude of stressor relief); and selectively distribute insights, instructions, and / or recommendations related to the detection of stressors in crops to various users associated with the crop supply chain via the sensitive plant platform.

[0015] For example, a computer system in collaboration with the sensitive plant platform can selectively distribute alerts to local users associated with crops near or containing these sensitive plants (e.g., farmers, agronomists, researchers) and / or users in various sectors or industries associated with the supply chain associated with these crops, such as users associated with the food, apparel or textiles, distribution (e.g., trucking, shipping, storage), health, government, insurance, and / or chemical industries.

[0016] In one embodiment, a system can: acquire images of agricultural fields (e.g., satellite or aerial color or infrared images); scan the images for signals indicative of sensitive plants (e.g., fluorescence in specific spectral characteristics of sensitive plants or characteristics of specific stressors present at sensitive plants); identify sensitive plant populations planted in specific geographic areas depicted in the images based on these characteristic signals (or "baseline signals") detected in the images; and interpret stressor pressure within the sensitive plant population based on the presence and / or amplitude of the optical signals present at the sensitive plants; and integrate a stress map for the specific geographic area that depicts the stress of a set of stressors in the sensitive plant population during a specific time period.

[0017] The system may continuously acquire images of sensitive plants in a sensitive plant population over time to integrate a time series of stress data for the specific geographic region. The system may then utilize these time series of stress data in combination with time series of stress data collected for many sensitive plant populations across many crops and / or many regions to predict future stress of stressors in the specific geographic region based on current stress maps. The system may also fuse the time series of stress data with other data collected for the specific geographic region (such as time series weather data and / or time series crop treatment data) to predict future stress of stressors in the specific region based on (current) detected stress, applied crop treatments, average air temperature, average humidity, average rainfall, etc. corresponding to the specific geographic region.

[0018] In addition, the system can estimate changes in expected crop yields based on detection and / or prediction of stressor pressure in the particular geographic region, such as based on historical crop data collected for the particular geographic region (including time series of stress data and / or corresponding time series crop yield data). Therefore, based on these predicted stressor pressures and / or changes in expected crop yields, the system can selectively generate and distribute prompts to users related to detection and / or mitigation of crops in the particular geographic region.

[0019] 2.1 Regional simulation In one embodiment, the system can utilize fluorescent signals expressed by sensitive plants distributed throughout a geographic region (such as sensitive plants sown in several crops and / or on agricultural fields within a geographic region) to derive insights into crop health within the geographic region, the spread and / or distribution of stressors (e.g., insects, fungi, dehydration, flooding, heat stress, cold stress, soil stressors), and / or crop yields of crops of different crop types.

[0020] For example, the system may: acquire images (e.g., spectral images) of sensitive plants in crops planted throughout a geographic region; extract features indicative of stress of one or more stressors in sensitive plants in the geographic region, such as the intensity of fluorescence at a particular wavelength; interpolate or extrapolate these stresses of stressors in sensitive plants to other plants (e.g., sensitive plants and non-sensitive plants) in the same geographic region; and detect and / or predict changes in these stresses of the set of stressors over time to predict crop yields for crops of each crop type in the geographic region. Thus, the system may: derive a yield profile for a geographic region that defines predicted crop yields for each crop type (e.g., soybeans, tomatoes, corn, cotton) present in the geographic region; and share the predicted yield profile with users associated with crops in the geographic region (e.g., farmers, food suppliers, food processing facility managers, pesticide and / or fertilizer manufacturers, etc.) to enable these users to better predict outcomes associated with crops in the particular geographic region.

[0021] 2.2 Local crop simulation Additionally or alternatively, in another embodiment, the system can utilize fluorescent signals expressed by sensitive plants sown in a particular crop or field to derive insights into crop health, the spread and / or distribution of stressors within the crop, and / or crop yield of the particular crop. In particular, the system can utilize crop data time series (including stress data time series, environmental data time series (e.g., weather data, processing data), yield data time series, etc.) collected during one or more growth cycles of the crop to: derive a high-resolution stress map for the crop (which represents stressors and / or pressure gradients at plants in various regions of the crop); predict future stressors in different regions of the crop; characterize the health of individual plants or groups of plants within the crop; predict the yield of the crop within a particular growth cycle based on the health of the plants in the crop over time; and / or recommend customized crop treatments that are predicted to alleviate stressors in the crop for different regions of the crop to more accurately adjust the health of the plants throughout the crop.

[0022] Furthermore, in this embodiment, the system may utilize a time series of crop data collected for the crop to derive a set of target plant conditions (e.g., soil pH, nitrogen levels in the soil, irrigation levels, fertilizer levels, pest population size, fungal growth) for plants in the crop and / or for specific groups of plants located in different regions of the crop, the set of target plant conditions being predicted to maximize crop yield for the specific crop. The system may then detect deviations from the set of target conditions in different regions of the crop (e.g., throughout the growth cycle) and, therefore, for embodiments in specific regions of the crop, selectively suggest treatments configured to drive plant conditions in these regions toward the set of target plant conditions. Thus, by prompting alignment of plant conditions throughout the crop with a set of target conditions derived for the specific crop, the system may drive crop yield toward the maximum crop yield predicted for the crop.

[0023] 3. Sensitive plants Typically, sensitive plants include a promoter-reporter pair configured to detect stressors present in sensitive plants and produce a detectable signal (e.g., in the electromagnetic spectrum) to indicate the presence of these stressors in sensitive plants or in the region of the crop in which the sensitive plants are generally located. In particular, sensitive plants can be genetically modified to include: a promoter gene sequence (hereinafter referred to as a "promoter") configured to be activated when a specific stressor is present (e.g., "associated" to a specific stressor); and a reporter gene sequence (hereinafter referred to as a "reporter") configured to be activated in response to activation of the promoter, encoding a specific molecule (e.g., a fluorescent molecule) configured to express a detectable signal (e.g., a fluorescent signal). The promoter-reporter pair can be incorporated into sensitive plants by genetic engineering techniques that associate the expression of a promoter that responds to a specific biological stress with a reporter that produces a measurable signal when the promoter is expressed.

[0024] In one embodiment, a sensitive plant can be configured to include a promoter-reporter pair configured to signal the presence of a specific biotic and / or abiotic stress experienced by the sensitive plant, such as pests, diseases, water, heat, soil health, and / or nutrient stress or deficiency. For example, a sensitive plant can be genetically modified to include a promoter having an activity associated with the presence of a stressor at the plant, such as fungi, bacteria, pests, heat, water (e.g., dehydration and / or overhydration), disease or nutrient stress (e.g., nutrient deficiency), phytoplasma disease, poor soil health (e.g., soil pH), etc. The sensitive plant can also be genetically modified to include a reporter paired with a promoter, and the reporter is configured to produce a detectable signal, such as an electromagnetic signal in the visible or infrared spectrum (e.g., a fluorescent signal) when the corresponding promoter is activated. For example, a sensitive plant can be genetically modified to fluoresce (i.e., absorb photons at one frequency and emit photons at a different frequency) in the presence of (and proportional to) the stress of a specific stressor. Thus, a promoter-reporter pair can generate a measurable signal of a specific biotic stress or trait in sensitive plants through expression of the reporter.

[0025] In another embodiment, a sensitive plant can be genetically modified to include multiple promoter-reporter pairs, each promoter-reporter pair indicating a specific biological process occurring in a sensitive plant cell in response to a specific stressor. For example, a sensitive plant can include: a first promoter-reporter pair including a first promoter indicating a first biological process associated with the presence of a water stressor, the first promoter being labeled with a red fluorescent protein reporter; and a second promoter-reporter pair including a second promoter indicating a second biological process associated with the presence of a fungal stressor, the second promoter being labeled with a yellow fluorescent protein reporter. Thus, a sensitive plant can signal the presence of multiple stressors via genetic modification of sensitive plant cells to include a set of promoter-reporter pairs.

[0026] In one variation, sensitive plants can be genetically modified to include a composite genetic sensing network representing a set of combinatorial promoter-reporter pairs. In this variation, sensitive plants can utilize a small number of reporters (e.g., fluorescent compounds) to monitor and detect a larger number of promoters and / or biological processes, and thus simplify the detection process by reducing the number of required reporters, because fluorescent compounds exhibit broad spectral characteristics, and it may be difficult to simultaneously measure and distinguish a large number of these fluorescent compounds.

[0027] 3.1 Variant: Plant Identifier In one variation, a sensitive plant population can be genetically modified to express a detectable baseline signal (e.g., a fluorescent signal) associated with sensitive plants in the sensitive plant population. In particular, the genome of each sensitive plant in the sensitive plant population can be genetically modified to include an identifier gene (hereinafter referred to as an "identifier") configured to express a baseline signal (e.g., a fluorescent signal) containing identification information of the sensitive plant (e.g., crop location, sensor type, crop type), for example, independent of the presence of a stressor at the sensitive plant in the sensitive plant population.

[0028] In one embodiment, a sensitive plant population can be configured to express baseline signal characteristics of sensitive plants growing in a specific area. For example, a first sensitive plant population can be configured to express a first baseline signal associated with a first geographic area, the first baseline signal corresponding to fluorescence within a first wavelength range. A second sensitive plant population can be configured to express a second baseline signal associated with a second geographic area, the second baseline signal corresponding to fluorescence within a second wavelength range different from the first wavelength range. Therefore, the system can: acquire an image and / or multiple images of a larger geographic area including the first geographic area and the second geographic area; distinguish sensitive plants in the first geographic area and sensitive plants in the second geographic area based on the detection of the first baseline signal and the second baseline signal in these images; thereby interpreting the pressure of the stressor, generating a stress map, deriving yield forecasts for both the first geographic area and the second geographic area, etc.

[0029] In particular, in one example, a sensitive plant population may be genetically modified to include a fluorescent tag configured to express a first fluorescent signal (e.g., red, yellow, and / or green fluorescence) within a first wavelength range (e.g., in the presence of light). The system may associate the fluorescent tag and the corresponding first fluorescent signal to a first region and / or crop containing the sensitive plant population. Specifically, in this example, the system may store a first fluorescent spectrum depicting the fluorescent signal within the first wavelength range in a first regional crop profile associated with the first region in a set of regional crop profiles. The system may then associate the sensitive plant and / or the sensitive plant population including the sensitive plant to the first fluorescent signal within the first wavelength range. Subsequently, the system may: acquire an image feed of a geographic region including the sensitive plant and / or the sensitive plant population recorded by an aerial sensor (e.g., a satellite); and identify the sensitive plant and / or the sensitive plant population in the image from the image feed based on detection of the first fluorescent signal (i.e., a baseline signal) expressed by the sensitive plant.

[0030] The system may also append the following to the first regional crop profile: a sensitive plant type of a sensitive plant in a sensitive plant population, such as a specific promoter-reporter pair configured to detect a signal for a specific stressor in a set of stressors; and / or a crop type (e.g., cotton, corn, soybean, grape) of a sensitive plant in a sensitive plant population. Then, a population of sensitive plants (e.g., sensitive plant seeds and / or sensitive plant seedlings) may be distributed (e.g., planted) in a crop and / or in multiple crops in a first region (e.g., by a farmer or other user associated with the crop). Subsequently (e.g., during the growth of a sensitive plant population in a crop and / or a crop), the system may acquire an image feed of the first region recorded by a satellite at approximately a target frequency (e.g., weekly, biweekly, monthly). Then, in response to detecting a fluorescent signal in a first image in the image feed, the system may: acquire the set of regional crop profiles; and in response to the detected fluorescent signal corresponding to a first fluorescent signal in a first wavelength range, identify the detected fluorescent signal as corresponding to a sensitive plant type and / or a sensitive plant population of a crop type located in the first region.

[0031] Furthermore, in the aforementioned example, the sensitive plant population may be genetically modified to include a promoter-reporter pair configured to perform signal detection of a first stressor at the sensitive plant, the promoter-reporter pair including: a promoter configured to be expressed in response to detection of the first stressor; and a reporter configured to express a second fluorescent signal in a second wavelength range different from the first wavelength range in response to expression of the promoter. Thus, in this example, the sensitive plant may be configured to include a first fluorescent tag configured to express a first fluorescent signal in a first wavelength range, such that expression of the first fluorescent signal does not interfere with detection of a second fluorescent signal indicating the presence of the first stressor, thereby reducing errors caused by detection of additive or overlapping fluorescent signals.

[0032] Additionally or alternatively, in another embodiment, each sensitive plant population can be configured to express a baseline signal associated with a specific crop type in a group of crop types. For example, a first sensitive plant population can be configured to express a first baseline signal associated with a first crop type (e.g., soybeans), the first baseline signal corresponding to fluorescence within a first wavelength range. A second sensitive plant population can be configured to express a second baseline signal associated with a second crop type (e.g., tomatoes), the second baseline signal corresponding to fluorescence within a second wavelength range different from the first wavelength range. Thus, the system can: acquire an image and / or multiple images of an area including crops of the first crop type and the second crop type; distinguish sensitive plants of the first crop type from sensitive plants of the second crop type based on detection of the first baseline signal and the second baseline signal in these images; thereby interpreting the stress of the stressor, generating a stress map, deriving yield predictions for crops of the first crop type and crops of the second crop type, etc.

[0033] Additionally or alternatively, in one variation, the sensitive plant may be genetically modified to include an identifier configured to express a detectable signal (e.g., a fluorescent signal) associated with a growth stage of the sensitive plant. Additionally, in this variation, the sensitive plant may be genetically modified to include a set of identifiers, each of which is configured to express one of a set of detectable signals associated with a specific growth stage of the sensitive plant. For example, a sensitive plant and / or a population of sensitive plants may be configured to include: a first identifier configured to express a first baseline signal during an initial growth stage (e.g., seed, seedling, and / or early vegetative stage); a second identifier configured to express a second baseline signal during a primary growth stage (e.g., budding and / or flowering stage); and a third identifier configured to express a third baseline signal during a final growth stage (e.g., maturity). Thus, the system can track the growth of the sensitive plant throughout its life cycle, and derive insights and information related to crop health and / or yield of a particular crop based on the detection of these baseline signals at various stages of the life cycle of the sensitive plant.

[0034] 4. Stressor Detection The system can detect and interpret signals generated by sensitive plants by extracting features from images of sensitive plants, the features being associated with the presence of a particular stressor at the sensitive plants. More specifically, the system can acquire digital images (e.g., spectral images) of sensitive plants and / or plant canopies (e.g., sensitive plants and surrounding plants) captured by an optical sensor (e.g., a multispectral or hyperspectral imaging device) to detect reporter signals and interpret stressors present in the sensitive plants based on the reporter signals.

[0035] In particular, the optical device can record (e.g., in the form of color or multispectral images) optical signals generated by sensitive plants; and the computer system can extract features from these images (e.g., intensity at specific wavelengths), interpret the presence and / or amount of specific stressors exposed to the sensitive plants based on these features, and interpolate or extrapolate the health status and environmental conditions of other nearby plants (e.g., non-sensitive plants; other sensitive plants not imaged) based on the presence and / or amount of stressors thereby indicated by the sensitive plants.

[0036] For example, a computer system can extract the intensity of specific wavelengths corresponding to specific compounds (e.g., proteins) in sensitive plants and interpret the stress of exposure to specific stressors in sensitive plants based on the intensity of these wavelengths (such as based on a stored model that associates plant stressors with wavelengths of interest, the stored model being based on known properties of promoter genes and reporter genes in sensitive plants) and before such stressors are visually discernible in the visible spectrum (i.e., with the human eye). The computer system can also interpolate or extrapolate the presence or amount of these stressors in other plants near the sensitive plant to predict the overall health of the crop or field.

[0037] In particular, in one example, a system may: acquire a first subset of images (e.g., one image, a series of images) of sensitive plants planted in an agricultural field within a geographic region, the sensitive plants being configured to signal stress of a set of stressors in the plants; extract a first set of fluorescence measurements (such as a first set of fluorescence intensities) corresponding to a first wavelength range from the first subset of images; extract a second set of fluorescence measurements (such as a second set of fluorescence intensities) corresponding to a second wavelength range from the first subset of images; interpret a first stress of a first stressor in a set of stressors in the plants within the geographic region based on the first set of fluorescence measurements; and interpret a second stress of a second stressor in the set of stressors in the plants within the geographic region based on the second set of fluorescence measurements.

[0038] The system can acquire images of sensitive plants captured by an optical sensor, such as from a handheld camera, a handheld spectrometer, a mobile phone, a UAV, a satellite, or from any other device, including a high-resolution spectrometer, a device including band-specific filters or otherwise configured to detect wavelengths of electromagnetic radiation fluorescence, luminescence, or any other optical signal emitted by sensitive plants in the presence of a specific stressor. More specifically, the system can: acquire hyperspectral images of leaf area of ​​sensitive plants, entire sensitive plants, a group of similar sensitive plants, an entire crop of sensitive plants, or many fields of sensitive plants recorded by a remote sensing system (e.g., satellite, in an aircraft, in manned or unmanned field equipment (such as a tractor), in a handheld device, in a boom or pole mounted in a field); extract spectral characteristics from these hyperspectral images; and interpret the presence and / or amount of a specific stressor present at a sensitive plant, a group of plants, a crop, or a field based on correlations between the spectral characteristics extracted from these hyperspectral images and known characteristics expressed by a specific promoter-reporter pair in the sensitive plant (e.g., fluorescence).

[0039] 5. Sensitive Plant Platform The system can compile large amounts of crop data (such as historical stress data and / or (near) real-time stress data, processing data, environmental data, and / or crop yield data) collected for sensitive plant populations planted, grown, and / or harvested across multiple crops and / or multiple regions (e.g., geographic areas) to derive insights and additional information related to crop health, supply, and / or management practices for a specific crop, a specific region, and / or the global crop supply.

[0040] The system can provide this data, instructions, insights, suggestions, information and / or recommendations to users associated with particular crops, crop regions and / or crop types (such as farmers, food suppliers, insurance agents (e.g., insurance agents associated with farm assessments), retailers, etc.), thereby enabling these users to monitor stressors in these crops and / or mitigate risks associated with these stressors.

[0041] 5.1 Crop Profile In one embodiment, the system can compile sensitive plant data collected over time for a specific crop and / or a specific region (e.g., including many crops) into a crop profile for the specific crop and / or region in a set of crop profiles. Thus, the crop profile can include information representing historical sensitive plant data for the crop, such as: the type of stressor detected, the pressure of the stressor detected, changes in the pressure of the stressor detected over time, etc.

[0042] The system may also collect additional information about the crop from the user, such as: the schedule on which the crop was planted; the schedule on which the crop was harvested; the frequency of irrigation; the amount of irrigation; the types of treatments typically applied to the crop; information on soil health; information on crop nutrients; etc. Thus, using this additional information, the system may estimate the stress of the crop with increased sensitivity and increased confidence, thereby enabling the user to have greater confidence in the information provided by the system and to gain additional and more accurate insights and information about the health of the crop.

[0043] 5.1.1 Baseline pressure curve In one variation, the system can identify patterns and / or trends in sensitive plant data collected over time (e.g., during a growing season) for a particular crop or region. In particular, in this embodiment, the system can utilize sensitive plant data collected for a particular crop or region over one or more growing seasons to derive a baseline stress curve (e.g., an annual stress curve) that represents detected or expected changes in stress of stressors in the particular crop or region during a particular time period (such as corresponding to a particular time of the growing season and / or year).

[0044] For example, during the first growing season of a crop, the system may: monitor reporter signals expressed by sensitive plants sown in the crop; use the detection of these reporter signals over time to derive a time series of stress for a set of stressors at set intervals (e.g., weekly, biweekly, monthly); and, based on the time series of stress for the set of stressors, extract insights and information about: water movement on the crop; sunlight exposure on the crop (e.g., daily, weekly, monthly, seasonal); and the timing and / or movement of stress from other stressors (such as insects, fungi, and / or nutrient deficiencies). Thus, the computer system may integrate the time series of stress and / or stressor data (corresponding to water movement, sunlight exposure, and / or other stressor stress) into a set of baseline stress curves for a set of stressors in that particular crop. The system may then derive these baseline stress curves to: predict the conditions of the crop and / or region at the beginning of a subsequent growing season and / or throughout a subsequent growing season; and / or recommend implementation of farming practices and / or stressor treatments at the crop based on the predicted plant conditions.

[0045] In one embodiment, during a first growth cycle of plants sown in a first area (e.g., a crop, a field, a geographic area), the system may: interpret a first set of pressures for a set of stressors at sensitive plants in the first area based on a first image or a first set of images captured at a first time and depicting the sensitive plants in the first area at the first time (each pressure in the first set of pressures corresponds to a specific stressor in the set of stressors); interpret a second set of pressures for the set of stressors at the sensitive plants in the first area based on a second image or a second set of images captured at a second time after the first time and depicting the sensitive plants in the first area at the second time; and interpret a third set of pressures for the set of stressors at the sensitive plants in the first area based on a third image or a third set of images captured at a third time after the second time and depicting the sensitive plants in the first area at the third time.

[0046] The system can then integrate a time series of pressure data representing changes in pressure of the set of stressors in the first region during a growth cycle, the time series of pressure data comprising: a first time value corresponding to a first time, the first time value being associated with a first set of pressures for the set of stressors; a second time value corresponding to a second time, the second time value being associated with a second set of pressures for the set of stressors; and a third time value corresponding to a third time, the third time value being associated with a third set of pressures for the set of stressors. Based on the time series of pressure data, the system can then derive a set of baseline pressure curves representing observed changes in pressure of the set of stressors over time. Subsequently, the system can use the set of baseline pressure curves to predict changes in pressure of the set of stressors over time. For example, the system may: derive a first baseline pressure curve from the set of baseline pressure curves representing changes in pressure of an insect stressor in the first area over time; derive a second baseline pressure curve from the set of baseline pressure curves representing changes in pressure of a fungal stressor in the first area over time; and / or derive a third baseline pressure curve from the set of baseline pressure curves representing changes in pressure of a soil stressor (e.g., soil pH, nitrogen level) in the first area over time.

[0047] The system may also refine this baseline pressure curve over time for a specific stressor in a specific region (eg, a specific crop, a specific geographic region), such as based on additional time series of pressure data collected for the specific stressor in the specific region (eg, a specific crop, a specific geographic region).

[0048] 5.2 Regional pressure map In one embodiment, the system can utilize the detection of stressors at sensitive plants (e.g., individuals, clusters, and / or crops of sensitive plants) sown in a specific area to generate a map representing the pressure of abiotic stressors and / or biotic stressors detected in the specific area during a corresponding detection time period.

[0049] For example, the system may: acquire in a satellite image feed (e.g., recorded by an optical sensor mounted on a satellite) a first image recorded during a first detection time period of a first crop of sensitive plants located in a first geographic area; acquire in the satellite image feed a second image recorded during the first detection time period of a second crop of sensitive plants located in the first geographic area; and acquire in the satellite image feed a third image recorded during the first detection time period of a third crop of sensitive plants located in the first geographic area.

[0050] The system may then: interpret a first stress of a first stressor in a first crop of sensitive plants from a set of stressors based on an optical signal (e.g., a fluorescent signal) expressed by a sensitive plant in the first crop of sensitive plants and detected in the first image; interpret a second stress of the first stressor in the first crop of sensitive plants based on an optical signal expressed by a sensitive plant in the second crop of sensitive plants and detected in the second image; and interpret a third stress of the second stressor in a third crop of sensitive plants from the set of stressors based on an optical signal expressed by a sensitive plant in the third crop of sensitive plants and detected in the third image. Then, at a first time after the first detection time period, the system may: interpret a first stress map indicating the presence of the set of stressors in the first geographic area during the first detection time period based on the first stress of the first stressor, the second stress of the first stressor, and the third stress of the third stressor.

[0051] Additionally and / or alternatively, in another embodiment, the system may generate a stress map for a specific stressor. For example, the system may: generate a first stress map indicating the presence of a first stressor in a set of stressors in a specific region; and generate a second stress map indicating the presence of a second stressor in the set of stressors in the specific region. Additionally and / or alternatively, in yet another embodiment, the system may generate a stress map for a specific crop type. For example, the system may: generate a first stress map indicating the presence of a set of stressors in crops of a first crop type (e.g., cotton) in a specific region; and generate a second stress map indicating the presence of the set of stressors in crops of a second crop type (e.g., soybean) in the specific region.

[0052] In each of these embodiments, the system can repeat this process over time to generate additional stress maps that represent the presence of a set of stressors in specific regions and / or in crops of specific crop types. By repeating this process over time, the system can provide data and / or recommendations to users associated with crops in these regions, thereby enabling proactive mitigation of stressors in these crops. In addition, the system can utilize the time series of stress data represented in these stress maps to derive insights and information related to changes in stress and / or stressors present in these regions (and / or specific crops) over time.

[0053] 5.2.1 Regional pressure map: interpolation In one embodiment, the system can use the detected and / or predicted pressure of stressors in plants at different locations (e.g., crops, fields, geographic locations) to interpolate the pressure in plants and / or crops planted between these locations. In particular, in this variation, the system can interpolate between the pressure of stressors derived for plants in a first sub-region and a second sub-region to predict one or more pressures of stressors present in plants planted in crops between the first sub-region and the second sub-region. The system can then generate a pressure map for a geographic region (including the first sub-region, the second sub-region, and a series of regions between the first sub-region and the second sub-region), the pressure map representing the predicted pressure of stressors across the sub-regions, regardless of whether sensitive plants are present in each of these sub-regions.

[0054] For example, the system may: interpret a first pressure of a first stressor in a first sub-region of a set of stressors in the geographic region based on features extracted from a first subset of images depicting sensitive plants in the first sub-region during a first time period in an image feed of sensitive plants in the geographic region; and interpret a second pressure of the first stressor in a second sub-region of the geographic region based on features extracted from a second subset of images depicting sensitive plants in the second sub-region during the first time period in the image feed. Then, the system may: predict a third pressure of the first stressor at a third sub-region of the geographic region based on the first pressure of the first stressor in the first sub-region and the second pressure of the first stressor in the second sub-region; and derive a first pressure map for the geographic region time period based on the first pressure, the second pressure, and the third pressure. Additionally and / or alternatively, in this embodiment, the system may similarly: predict a fourth pressure of the first stressor at a fourth sub-region, predict a fifth pressure of the first stressor at a fifth sub-region, predict a sixth pressure of the first stressor at a sixth sub-region, etc.; and update the first pressure map accordingly.

[0055] In one variation, the system may utilize environmental data collected for crops within a geographic region to more accurately predict stressors pressure in sub-regions throughout the geographic region.

[0056] 5.2.2 Real-time pressure prediction In one embodiment, the system can use the detection of a first pressure of a specific stressor within a first area (e.g., a crop, a farmland, a geographic area) to predict a second pressure of the specific stressor in a second area that is proximate to the first area (e.g., adjacent to the first area, bordering the first area, near the first area). For example, the system can: implement the above methods and techniques to predict a first pressure of a stressor in a first farmland growing crops of a first crop type; and predict a second pressure of the first stressor in a second farmland adjacent to the first farmland and growing crops of the first crop type based on known and / or predicted patterns of movement, diffusion, growth, attenuation, etc. of the first stressor.

[0057] In one example, a system may: interpret a first pressure of a first stressor in a first crop based on features extracted from an image of the first crop captured at a first time, the first crop comprising a set of sensitive plants configured to signal pressure of the first stressor; and use the first pressure of the first stressor to predict a second pressure of the first stressor in a second crop at approximately the first time based on a known and / or derived correlation between the pressure of the first stressor in the first crop and the pressure of the first stressor in a second crop.

[0058] In one variation, the system may derive a stress model that relates the stress of a set of stressors in a first area (eg, crop, field, geographic region) to the stress of the set of stressors in a second area.

[0059] For example, during a first growth period in a series of growth periods, the system may: acquire an image feed of a set of sensitive plants captured during the first growth period, the set of sensitive plants being sown in a first crop and being configured to signal a pressure of a set of stressors at the set of sensitive plants; and interpret a first time series of pressure data, based on features extracted from time-stamped images in the image feed, the first time series of pressure data representing changes in the pressure of the set of stressors at the set of sensitive plants throughout the first growth period. For example, the first time series of pressure data may define: a first pressure of a first stressor in the set of stressors at a first time during an initial time period; a second pressure of the first stressor at a second time during the initial time period after the first time; a third pressure of the first stressor at a third time during the initial time period after the second time; and so on. The first time series of pressure data may similarly include: a time series of pressure of a second stressor in the set of stressors throughout the initial time period; a time series of pressure of a third stressor in the set of stressors throughout the initial time period; and so on.

[0060] Furthermore, in the foregoing example, the system may: prompt one or more users (e.g., farmers, crop owners, crop operators) associated with a second crop (proximate to the first crop and excluding sensitive plants) to manually input a second time series of stress data captured for the second crop during the initial time period. For example, the farmer and / or agronomist may: record a time series of the amount of moisture in the soil and / or plants of the second crop; record a time series of nitrogen uptake in the plants within the second crop; record a time series of pH levels of the soil within the second crop; record a time series of the density and / or presence of insects and / or fungi within the second crop; and the like. Upon receiving this compiled time series of stress data for the second crop from the user, the system may: implement regression, machine learning, deep learning, and / or other techniques to derive correlations between the stress of the set of stressors in the first crop and the stress of the set of stressors in the second crop; and store these correlations in a stress model configured to predict the stress of the set of stressors in the second crop based on the detected stress of the set of stressors in the first crop.

[0061] 5.3 Pressure prediction: changes over time In one variation, the system may predict future stress of a stressor in plants within a particular region based on current stress of the stressor in plants within the particular region. In particular, in this variation, the system may: interpret a first stress of a first stressor in a first crop based on features extracted from an image of the first crop captured at a first time, the first crop comprising a set of sensitive plants configured to signal stress of the first stressor; and predict a second stress of the first stressor in the first crop at a second time after the first time based on observed and / or predicted diffusion (e.g., dispersion or movement, growth, decay) of the first stressor within the first crop over time.

[0062] In one embodiment, the system may derive a stress model configured to predict future stress of stressors in a particular crop and / or region based on the (current) detected stress of these stressors in the particular crop and / or region. In particular, the system may predict current or future stress of stressors in a particular geographic region using a time series of stressor data representing the presence and / or magnitude of a set of stressors detectable in sensitive plants in the particular geographic region over a particular time period.

[0063] For example, during a first time period, the system may: explain a first pressure of a first stressor within a first sub-region of a geographic region; explain a second pressure of the first stressor within a second sub-region of the geographic region; and explain a third pressure of the first stressor within a third sub-region of the geographic region. The system may then derive a first pressure map based on the first pressure, the second pressure, and the third pressure, the first pressure map indicating the presence of the first stressor within the geographic region during the first time period. Then, during a second time period, the system may: explain a fourth pressure of the first stressor within the first sub-region; explain a fifth pressure of the first stressor within the second sub-region; and explain a sixth pressure of the first stressor within the third sub-region. The system may then derive a second pressure map based on the fourth pressure, the fifth pressure, and the sixth pressure, the second pressure map indicating the presence of the first stressor within the geographic region during the second time period. The system may then derive a pressure model using the first pressure map and the second pressure map, the pressure model being configured to predict changes in the pressure of the first stressor within the geographic region as a function of time.

[0064] Therefore, in the aforementioned example, during a third time period after the second time period, the system may: derive a third pressure map based on the detected pressure of the first stressor in the first sub-region, the second sub-region and / or the third sub-region, the third pressure map indicating the presence of the first stressor in the geographic area during the third time period; obtain a pressure model; and based on the third pressure map and the pressure model, predict a fourth pressure map indicating the presence of the first stressor in the geographic area during a fourth time period after the third time period.

[0065] Furthermore, in another embodiment, the system may additionally obtain a time series of environmental data for a specific crop and / or region corresponding to the time series of stressor data, such as a time series of weather data and / or a time series of processing data (e.g., crop processing data). In this embodiment, the system may fuse the time series of environmental data with the time series of stressor data to derive a stress model, which is configured to predict the future stress of stressors and / or changes in the stress of stressors in a specific crop and / or region based on the detected stress of stressors and / or environmental data (e.g., current crop processing, weather, crop management information) for the specific crop and / or region.

[0066] Thus, the system can derive correlations between stressor data (e.g., recorded stressor pressures over time) for a particular crop and / or region and recorded environmental data to: identify the stressor treatment that best matches the particular crop and / or region; and predict changes in stressor pressure in the particular crop and / or region based on the detected stressor pressure and / or recorded environmental data. Thus, by tracking historical stressor data for the particular region or crop over time (e.g., within a month, within a crop season, over multiple crop seasons), the system can derive correlations that are configured to predict future stressor pressures in the particular region or crop, thereby enabling users associated with the crop (e.g., farmers, agronomists, field workers) to quickly implement techniques that are configured to mitigate and / or prevent stressors from these stressors and thereby increase crop yields and / or reduce costs and / or crop losses associated with treating stressors in the crop.

[0067] 5.3.1 Environmental data: Diffusion conditions In one variation, the system may utilize a time series of environmental data recorded for a particular area (eg, a crop, a field, a geographic region) to predict the current and / or future pressure of stressors within that particular area.

[0068] In one embodiment, the system may predict changes in pressure of a particular stressor over time based on a time series of environmental data. In particular, the system may: predict a first pressure of a first stressor present in plants sown in an area of ​​a crop based on a first image of a sensitive plant in the area captured at a first time; obtain a first time series of environmental data (such as including weather data, process data, observed stressor data, etc.) corresponding to environmental conditions in the area of ​​the crop during a first time period including the first time; and predict a second pressure of the first stressor at a future time based on the first pressure and the first time series of environmental data. In particular, the system may predict the second pressure of the first stressor at a future time using a known and / or derived correlation between the environmental data and changes in pressure of the first stressor over time.

[0069] Furthermore, in the aforementioned embodiments, the system may utilize the detected pressure of a particular stressor in a first region to predict the pressure of the particular stressor in a second region (eg, proximate to the first region) based on environmental data recorded for each of these regions.

[0070] Additionally or alternatively, in another embodiment, the system can utilize known characteristics of a particular stressor or multiple stressors to predict changes in the pressure of a particular stressor over time. For example, at a first time during the growing period of crops in a particular area, the system can: predict a first pressure of a fungal stressor at a first sub-area within the particular area; and predict a second pressure of a fungal stressor at a second sub-area within the particular area. The system can then: obtain a set of diffusion conditions defined for the fungal stressor and defining conditions required for the fungal stressor to spread between plants in the particular area, such as minimum and / or maximum temperature (e.g., air temperature), minimum and / or maximum rainfall, minimum and / or maximum sunlight, a set of soil conditions (e.g., nitrogen level, pH level), maturity of plants in the particular area, etc. The system can then: obtain a time series of environmental data captured for a particular area during a time period including a first time, including a time series of conditions (e.g., weather, treatment) at plants in the particular area; characterize a correlation between the set of diffusion conditions and the time series of environmental data; and, in response to the correlation exceeding a threshold correlation, predict diffusion of the fungal stressor within the particular area, such as at a first rate and / or according to a first pattern or distribution. Based on the predicted diffusion, the system can therefore predict: a third pressure of the fungal stressor at the first sub-area at a second time after the first time; and a fourth pressure of the fungal stressor at the second sub-area at the second time.

[0071] 5.4 Production Forecast In one embodiment, the system can predict the yield of a specific crop type (eg, soybean, tomato, cotton, lettuce) and / or a specific crop in a specific geographic region.

[0072] In general, the system can predict the yield of crops of a specific crop type based on the stress of stressors present in the crops. In particular, the system can: use images of crops in a geographic area to detect optical signals expressed by sensitive plants present in the crops; based on the detected optical signals, predict the stress of a set of stressors throughout the geographic area; based on the predicted stress of the set of stressors, derive a pressure map representing the magnitude and / or distribution of the stress of the set of stressors throughout the geographic area; and based on the derived pressure map, predict the crop yield of a specific crop type in the geographic area.

[0073] For example, the system may: predict a first stress of a first stressor in a first crop of a first crop type in a geographic region (including a first set of sensitive plants configured to signal the presence of the first stressor); predict a second stress of the first stressor in a second crop of the first crop type in the geographic region (including a second set of sensitive plants configured to signal the presence of the first stressor); predict a third stress of the first stressor in a third crop of the first crop type in the geographic region (including a third set of sensitive plants configured to signal the presence of the first stressor); and derive a pressure map representing the pressure of the first stressor throughout the geographic region based on the first pressure, the second pressure, and the third pressure. The system may then predict the crop yield of the crop of the first crop type in the geographic region based on the pressure map. The system may then repeat the process to similarly predict the crop yield of each crop type in a set of crop types present in the geographic region.

[0074] In one variation, the system may utilize environmental data (including weather data, treatment data, and / or observed stressor data) recorded for a geographic region in conjunction with a stress map to predict crop yields. For example, the system may: derive a stress map representing conditions (such as soil conditions (e.g., soil pH, nitrogen content), insect or fungal presence, disease, drought or flooding, etc.) across a geographic region of a group of crops including a first crop type; obtain a time series of environmental data recorded for the geographic region during a growing period; obtain a time series of treatment data recorded for the geographic region during a growing period, such as the type and / or dosage (e.g., amount, duration) of treatments (e.g., pesticides, fungicides, water, soil pH adjusters, fertilizers) applied to one or more crops; and based on the stress map and the time series of environmental data, predict the crop yield of the first crop type in the geographic region.

[0075] 5.4.1 Production Model In one embodiment, the system may utilize a yield model to predict the yield of a particular crop type (e.g., cotton, soybeans, corn) in a particular crop, in a particular region, and / or globally. The system may then selectively distribute insights, instructions, and / or recommendations to various users associated with various sectors of the agricultural supply chain based on the predicted yield of the particular crop type.

[0076] In this embodiment, the system can derive a yield model configured to predict the crop yield of a specific crop, a specific region, and / or a specific crop type within a specific region. In particular, the system can: record a time series of stress data representing the stress of a set of stressors within a region (e.g., a crop, a geographic region) throughout an initial growth period; record the final crop yield of a crop of a crop type (e.g., soybeans, cotton, lettuce, tomatoes) within the region, such as the final crop yield recorded during and / or after harvesting the crop of the crop type at the end of the initial growth period; store the time series of stress data and the final crop yield in a first growth period grouping (e.g., a data grouping); and derive a yield model that associates the stress of a set of stressors within a geographic region with the crop yield of the crop of the crop type within the geographic region based on the time series of stress data and the final crop yield.

[0077] The system may further repeat the process during subsequent growing periods of crops of the crop type in the geographic area to further refine the yield model. Specifically, the system may: generate a series of growing periods, such as including a first growing period grouping, a second growing period grouping generated for a second growing period, a third growing period grouping generated for a third growing period, and the like; and based on each growing period grouping in a series of growing period groupings generated for crops of the crop type in a particular area, revise the yield model. In addition, the system may perform the process to derive a yield model for each crop type of crop present in the area. For example, the system may: derive a first crop yield model, which is configured to predict the crop yield of a tomato crop in the area; derive a second crop yield model, which is configured to predict the crop yield of a soybean crop in the area; derive a third crop yield model, which is configured to predict the crop yield of a cotton crop in the area; and so on.

[0078] For example, the system may: obtain a first time series of stress data, the first time series of stress data representing changes in stress of a set of stressors in a first region during a first growing period; obtain a first crop yield of a crop of a first crop type (e.g., cotton) in the first region during a first harvest period corresponding to the first growing period; obtain a second time series of stress data, the second time series of stress data representing changes in stress of the set of stressors in the first region during a second growing period after the first harvest period; obtain a second crop yield of a crop of the first crop type (e.g., cotton) in the first region during a second harvest period corresponding to the second growing period. The system may then implement regression, machine learning, deep learning, and / or other techniques to derive a correlation between stress of stressors in the particular region and / or in a particular crop in the region and crop yield.

[0079] 5.5 Production Profile In one variation, the system may derive a yield profile representing predicted yields for crops of a set of crop types (eg, soybeans, tomatoes, cabbage, corn, cotton) within a particular region.

[0080] In particular, in this variation, the system may: implement the above methods and techniques to interpret the pressure of a set of stressors in various sub-regions within a specific region and accordingly derive a pressure map for the specific region; based on the pressure map, predict the crop yield in a set of crop yields for each of a set of crop types; and compile the set of crop yields into a yield profile for the specific region, the yield profile defining the predicted crop yield for each of the set of crop types. Over time, the system may update the yield profile based on detected and / or predicted changes in the pressure of the set of stressors throughout the geographic region.

[0081] For example, during a growing season, a system may: obtain a first set of images captured during a first time period within the growing season depicting susceptible plants sown in crops within a first geographic region; interpret the stress of a set of stressors within the geographic region based on features extracted from the first set of images; and accordingly integrate a first stress map depicting the stress of the set of stressors within the first geographic region during the first time period. The system may then: predict a first crop yield for a crop of a first crop type (e.g., soybeans) in a first set of crop yields based on a first subset of features extracted from an area of ​​the first stress map corresponding to crops of the first crop type (e.g., soybeans); generate a first yield profile in a set of yield profiles for the first geographic region, the first yield profile representing a predicted yield of crops of the set of crop types during the first time period; and store a first crop yield associated with the first crop type in the first yield profile.

[0082] The system can also: predict a second crop yield for a crop of a second crop type (e.g., tomatoes) in the first group of crop yields based on a second subset of features extracted from an area of ​​the first pressure map corresponding to the crop of the second crop type; store the second crop yield associated with the second crop type in the first yield profile; and repeat the process for each crop type in the group of crop types to integrate a comprehensive yield profile for the first geographic area during the first time period.

[0083] Subsequently, during the growing period, the system may: acquire a second set of images captured during a second time period after the first time period during the growing period, the second set of images depicting sensitive plants sown in crops within the first geographic area; interpret stress of a set of stressors within the geographic area based on features extracted from the second set of images; accordingly integrate a second pressure map depicting stress of a set of stressors within the first geographic area during the first time period; predict crop yields in a second set of crop yields for crops of each crop type in a set of crop types based on features extracted from regions of the second pressure map corresponding to crops of each crop type; and generate a second yield profile in a set of yield profiles for the first geographic area, the second yield profile representing predicted yields for crops of the set of crop types during the second time period.

[0084] The system may then: store a second set of crop yields (each crop yield associated with a specific crop type) in a second yield profile; associate the first yield profile with a first time value (e.g., a timestamp) corresponding to the first time period; associate the second yield profile with a second time value corresponding to the second time period; thereby deriving a time series of crop yield profiles for the first geographic region, including the first yield profile associated with the first time value (e.g., a timestamp) corresponding to the first time period and the second yield profile associated with the second time value corresponding to the second time period. The system may repeat this process throughout the remainder of the growing season and for each subsequent growing season to derive a comprehensive time series of crop yield profiles for the first geographic region.

[0085] 5.6 Crop Health In one embodiment, the system can utilize time series of crop data (e.g., stress, stressor and / or environmental data) collected for a specific crop and / or a specific region to characterize the health status of the specific crop and / or the specific region (hereinafter referred to as "crop health status").

[0086] For example, the system may acquire a first set of crop data corresponding to operation of a first crop located in a first area during a first time period, including: the number of stressors detected during the first time period; the average stress of each stressor detected; the resistance of the crop to each stressor detected (e.g., the average duration of recovery); the total yield of the crop during a harvest season corresponding to the first time period; and / or the quality of the harvested crop (such as assessed by a user associated with the crop during the harvest season); etc. The system may then characterize a health score of the crop (e.g., a percentage between 0% and 99%, a scale value from 1 to 10, categorized as "poor", "fair", "good", or "excellent") based on the first set of crop data collected during the first time period.

[0087] In the foregoing example, the system can repeat the process for multiple crops and / or crop regions to integrate a health map representing the health of the crops in each region and / or across these regions during a specific time period. In addition, the system can update the health map over time (such as over multiple growing seasons).

[0088] In one variation, a system may predict crop yields of crops of a crop type in a particular region based on predicted crop health conditions of crops of the crop type in the particular region. In particular, in this variation, the system may: characterize plant health conditions of crops of the crop type in the region based on a set of crop data collected for the crops of the crop type in the region (such as including stress data time series and / or environmental data (e.g., processing data, weather data) time series); obtain a yield model that relates plant health conditions of crops of the crop type to crop yield; and predict crop yields of crops of the crop type (e.g., at the end of the current growing season) based on the plant health conditions of crops of the crop type in the region and the yield model.

[0089] For example, at a first time during the growing period, the system may: obtain a first time series of crop data recorded for plants of a first crop type in a group of crop types sown in a first sub-area of ​​the area during a first time period before the first time, including a first time series of pressure data and a first time series of environmental data; obtain a second time series of crop data recorded for plants of the first crop type sown in a second sub-area of ​​the area during the first time period, including a second time series of pressure data and a second time series of environmental data; and obtain a third time series of crop data recorded for plants of the first crop type sown in a third sub-area of ​​the area during the first time period, including a third time series of pressure data and a third time series of environmental data. The system can then: predict a first health score for crops of a first crop type in a first sub-region in a set of health scores based on a first time series of crop data; predict a second health score for crops of the first crop type in a second sub-region in the set of health scores based on a second time series of crop data; predict a third health score for crops of the first crop type in a third sub-region in the set of health scores based on a third time series of crop data; and predict (such as at a future time after the first time (e.g., at the end of the growing season)) a first crop yield of crops of the first crop type based on a yield model and the set of health scores.

[0090] 5.7 Tips In one embodiment, the system can selectively generate and distribute prompts related to the detection and mitigation of stress from stressors within a crop or within a specific region. In particular, the system can identify and suggest stress mitigation techniques configured to: reduce the stress of detected stressors within a crop or within a region; minimize the spread of detected stress within a crop or across a specific region; and maximize crop yield within a specific crop and / or across a specific region.

[0091] For example, as described above, the system may derive a first pressure map for a particular region, the first pressure map indicating the locations of various pressures of a stressor detected during a first time period. The system may then: predict a second pressure map indicating predicted locations of predicted pressures of a stressor detected during a second time period after the first time period based on the first pressure map and a pressure model generated for the particular region; and / or estimate a predicted crop yield of a crop of the first crop type in the particular region based on the first pressure map, the second pressure map, and a yield model that relates the pressure maps to the crop yield of the crop of the first crop type in the particular region.

[0092] The system can then send a set of prompts to a group of crop operators associated with the crops in the specific area based on the first stress map to perform a set of mitigation techniques configured to increase the efficiency of crop treatment and maintenance over time and / or maintain or increase the yield of the crop. In addition, the system can update a crop profile associated with the crops in the specific area based on the first stress map, the second predicted stress map, and the predicted crop yield. The system can then send this data to additional end users (e.g., crop treatment developers, clothing retailers, food retailers) who are associated with and / or interested in the yield and / or health of the crops in the specific area.

[0093] 5.7.1 Treatment effect In one embodiment, the system can characterize the effectiveness of a crop treatment based on detected changes in the pressure of a particular stressor or multiple stressors.

[0094] For example, the system may: interpret a first stress of a first stressor in a crop of a particular crop type based on features extracted from a first image of the crop recorded during a first time period; and in response to interpreting the first stress of the first stressor, generate a prompt to apply a first treatment (such as a specific dose and / or type of treatment (e.g., pesticide, fungicide, irrigation, fertilizer, soil pH treatment)) to the crop during a second time period after the first time period, and send the prompt to a user (e.g., farmer) associated with the crop. The system may then: interpret a second stress of the first stressor in the crop based on features extracted from a second image of the crop recorded during a third time period after the second time period; characterize a difference between the first stress and the second stress of the first stressor; and based on the difference, characterize an effect of the first treatment in alleviating the stress of the first stressor.

[0095] For example, in response to the difference exceeding a threshold difference, such that the detected stress of the first stressor decreases (e.g., decreases by more than a threshold amount) in response to application of the first treatment, the system can characterize the first treatment as being highly effective in alleviating stress of the first stressor. However, in response to the difference falling below the threshold difference, such that the detected stress of the first stressor increases and / or exhibits a minimal decrease (e.g., decreases by less than a threshold amount) in response to application of the first treatment, the system can characterize the first treatment as being ineffective in alleviating stress of the first stressor. The system can store this information in a crop profile for the crop to inform future treatment recommendations for the first stressor in that particular crop, crops of that crop type, and / or crops of all crop types.

[0096] 5.8 Supply Chain Tracking In one embodiment, the system can track the distribution of a population of sensitive plants that have been modified to include specific identifiers throughout the life cycle of these sensitive plants (e.g., along the crop supply chain), such as from the growing period within the crop, to loading onto a specific transportation vehicle, to storage within a specific storage facility, to processing within a specific facility, etc., as the sensitive plants are transferred through the crop supply chain.

[0097] For example, for a first sensitive plant including a fluorescent tag configured to express a baseline fluorescent signal, the system may: obtain a first image (e.g., a hyperspectral image) in a set of images captured by a farmer or agronomist (e.g., via a mobile device) associated with a crop containing the sensitive plant, the first image including a first timestamp and depicting the baseline fluorescent signal; obtain a second image in the set of images captured by an optical sensor disposed within a crop storage facility, the second image including a second timestamp and a batch identifier for a batch containing the sensitive plant, and depicting the baseline fluorescent signal; obtain a third image in the set of images captured by an optical sensor installed within a processing and / or manufacturing facility (e.g., a food manufacturing and / or processing facility, a clothing manufacturing facility), the third image including a third timestamp and depicting the baseline fluorescent signal; and obtain a fourth image in the set of images captured by an optical sensor installed within a retail facility (e.g., a grocery store, a clothing store), the fourth image including a fourth timestamp and depicting the baseline fluorescent signal. Thus, the system may associate the sensitive plant (e.g., a product of the sensitive plant) sold in the retail facility to a crop and / or region of origin based on the detection of the baseline fluorescent signal in the set of images.

[0098] Over time, based on these associations, the system can acquire additional information about crop yield and crop distribution, and thus update crop profiles for specific crops and / or specific crop areas based on this information. The system can then use this information to derive more precise insights or information related to crop yield, crop supply, crop health, crop quality, and / or crop management for specific crops and / or crop areas. For example, a soybean plant crop (e.g., a sensitive soybean plant) can be modified to express a baseline fluorescence signal that is configured to fluoresce only in soybean pods, thereby enabling soybean yield to be reported directly through the soybean plant crop throughout the life cycle of these soybean plants (such as during growth, harvesting, and / or distribution throughout the supply chain). In particular, the system can: acquire images of soybean plants sown in the crop and / or harvested from the crop; characterize the magnitude (e.g., intensity) of the baseline fluorescence signal detected in these images; and estimate the yield of the soybean crop based on the magnitude of the baseline fluorescence signal. The system can then repeat this process to update the yield estimate of the soybean crop over time.

[0099] Further, in this embodiment, the system can utilize this information to: identify subsets of users associated with the supply chain for particular crops and / or crop regions; and, over time, selectively send information, insights, instructions, recommendations, and / or suggestions to these different subsets of users, such as information, insights, instructions, recommendations, and / or suggestions related to actual and / or predicted crop yields, crop supply, crop health, crop quality, crop management, and the like.

[0100] For example, the system may identify a subset of users associated with manufacturing garments from cotton grown in a particular region based on receiving an identification of a baseline fluorescent signal associated with cotton grown in a particular region in cotton stored in a manufacturing plant associated with the subset of users. The system may then selectively provide notifications and / or prompts to the subset of users during the next growing season of the cotton in the particular region, such as in response to: detecting a large stressor in the region; in response to predicting that the crop supply of cotton in the particular region is very low; in response to predicting that the crop supply of cotton in the particular region is very high; etc. Thus, the system may then provide the subset of users with valuable insights or information in (near) real-time regarding the derived data and / or predictions regarding the cotton supply for the particular subset of users and the cotton supply from the particular region during the growing season.

[0101] In one variation, the system can utilize the detection of the baseline signal to distinguish between plants (e.g., sensitive plants) in the crop and weeds growing in the crop. For example, the system can: acquire an image of a crop including a set of sensitive plants configured to express a baseline fluorescent signal recorded by an optical sensor (e.g., an optical sensor mounted on manned or unmanned field equipment (such as a tractor), an optical sensor on a handheld device operated by a farmer or agronomist, and / or an optical sensor mounted on a boom or pole in the field); isolate areas in the image corresponding to the absence of the baseline fluorescent signal; and mark these areas for application of a specific weed treatment (such as by alerting a farmer to apply the specific weed treatment and / or by triggering the application of the weed treatment by automated field equipment (e.g., an automated sprayer mounted on a tractor)) and / or for further review. The system can repeat this process periodically (e.g., daily, weekly) to review the presence of weeds and / or other threats (e.g., invasive plants) in the crop, thereby reducing risk to the crop and achieving an increase in yield.

[0102] 6. Simulate plant health status In one embodiment, the system may utilize sensitive plant data collected over time from specific sensitive plants or groups of sensitive plants growing within a specific crop to characterize the plant health of individual plants, groups of plants, and / or all plants in a specific crop.

[0103] In particular, in this embodiment, the system can focus on a specific set of conditions (e.g., environmental conditions) that are indicative of plant health of plants in the specific crop (e.g., a single sensitive plant or a group of sensitive plants within the specific crop and / or the entire sensitive plant crop), such as the presence of a set of stressors (e.g., abiotic stressors and / or biotic stressors), soil conditions (e.g., pH levels, nitrogen levels), weather conditions, etc. Thus, the system can: detect signals (e.g., fluorescent signals) expressed by a group of sensitive plants in the specific crop; based on these signals, predict a set of current conditions at the group of sensitive plants; and interpret plant health based on the set of current conditions detected at the group of sensitive plants, such as individual plant health of each sensitive plant in the group of sensitive plants and / or a composite plant health of the group of sensitive plants.

[0104] Based on the predicted plant health, the system can select specific crop treatments that are customized for specific plants or groups of plants within the crop and configured to improve or maintain plant health.

[0105] Thus, in this embodiment, the system can utilize sensitive plant data collected over time for one or more sensitive plants sown in a particular crop to predict conditions at plants throughout the particular crop, such as corresponding to the magnitude and / or distribution of plant stressors (e.g., soil pH levels, nitrogen uptake, water retention, insect presence, fungal presence), and thereby characterize the health of individual plants or groups of plants with high resolution, thereby enabling customized treatment of specific groups of plants in the crop in response to changes in plant health.

[0106] 6.1 Target conditions In one embodiment, the system may define a set of target plant conditions (e.g., soil pH, nitrogen level in soil, irrigation level, fertilizer level, fungicide level) for a particular crop. Typically, in this embodiment, the system may: derive a set of target plant conditions that produce at least a target crop health condition, such as a set of target plant conditions corresponding to a threshold crop yield (e.g., a minimum crop yield); and store these sets of target plant conditions as constituting a "healthy crop" or "healthy plant" (e.g., in a crop profile generated for the particular crop).

[0107] In particular, in this embodiment, during an initial period of time, the system may: track plant conditions in sensitive plants located within the crop, such as the amount of insect pressure, the amount of fungal pressure, the amount of drought stress (e.g., dehydration), plant nitrogen content (e.g., the amount of nitrogen consumed by the plant from the soil), etc.; and analyze these data to derive correlations between plant conditions and plant health. Thus, the system may: derive a set of target plant conditions for plants in the crop that produce at least a minimum or target plant health; designate sensitive plants to signal plant conditions that are close to the set of target plant conditions that qualify as "healthy" plants (e.g., within a threshold deviation of the set of target plant conditions), such as by outputting a set of fluorescent signals within a specific wavelength range associated with the plant conditions; and during the growing season, implement the set of target plant conditions to selectively distinguish "healthy" plants in the crop from "unhealthy" or "less healthy" plants in the crop that may result in crop losses (e.g., reduced crop yields) if not addressed.

[0108] In one example of the foregoing implementation, the system may: track a time series of plant conditions of sensitive plants growing within a crop during an initial time period, such as including pressure data, soil data, weather data, etc.; obtain a time series of crop data recorded for plants in the crop during the initial time period, such as including plant health data (e.g., observed growth, wilting, plant death, color change, fruit growth) and / or yield data; calculate a correlation between plant conditions and plant health of sensitive plants in a particular crop based on the time series of plant conditions and the time series of crop data; and define a set of target plant conditions associated with a threshold plant health, such as a set of target plant conditions associated with a threshold probability of achieving a threshold crop yield, based on the correlation. More specifically, in this example, the system may implement machine learning, artificial intelligence, neural networks, or other analytical techniques to characterize plant health as a function of plant conditions based on detected plant conditions and observed health data of sensitive plants in the particular crop. Based on a target or threshold health score (e.g., a minimum health score) defined for plants in a particular crop, such as a target or threshold health score predicted to achieve a threshold crop yield such as specified by an advertiser for an advertising campaign, the system can derive a set of target conditions that are predicted to produce that threshold health score for plants in the crop.

[0109] 6.1.1 Example: Nitrogen content in soil In one example, the system can define a target nitrogen content for plants in a particular crop. In particular, in this example, the crop (e.g., a soybean crop, a corn crop, a cotton crop) can include a group of sensitive plants, such as a group of sensitive plants planted in clusters in various areas of the crop and / or throughout a field, such that the group of sensitive plants includes each plant in the crop configured to signal soil conditions at the group of sensitive plants. In particular, in this example, each sensitive plant in the group of sensitive plants can include: a promoter associated with the presence of nitrogen at the plant; a reporter (e.g., a fluorescent protein) associated with the promoter, the reporter configured to express a fluorescent signal representing the absorption of nitrogen by the sensitive plant. Therefore, in this example, the system can: detect the fluorescent signal (such as in an image of the sensitive plant recorded by a satellite), and interpret the amount of nitrogen consumed by the sensitive plant based on the intensity of the fluorescent signal.

[0110] During the first growing season of the particular crop, the system can use a global target nitrogen content, such as a universal nitrogen content defined for substantially all plants or all plants of a particular crop type (e.g., soybeans, corn, cotton) to predict plant health throughout the first growing season. In particular, the system can: acquire an image feed of the crop (such as recorded by a satellite); detect fluorescent signals expressed by a set of sensitive plants in the crop and depicted in images in the image feed; interpret the amount of nitrogen absorbed by sensitive plants in the set of sensitive plants based on the fluorescent signals; and characterize the plant health (such as across the entire crop and / or in a specific sub-region of the crop) based on the amount of nitrogen absorbed by the sensitive plants in the crop and the global target nitrogen content.

[0111] Over time, as the system collects additional data for sensitive plants in the crop (such as including a time series of nitrogen levels, a time series of nitrogen treatment data (e.g., frequency and amount of nitrogen applied to the soil), plant health data, and / or crop yield data), the system can focus on a crop-specific and / or plant-specific target nitrogen level, such as by updating a generic nitrogen level based on this additional data collected over time. For example, during a first growing season, the system can collect (such as at different times throughout the first growing season) surveys completed by users associated with the crop, and the surveys indicate plant health of plants throughout the crop, such as visual characteristics of plants in a given crop (e.g., color, wilting, height, fruit size, fruit growth), treatments applied to the crop (e.g., nitrogen, pesticides, fungicides, irrigation), crop losses, crop yields, etc. Based on this data, in combination with the nitrogen levels detected in sensitive plants throughout the crop, the system can focus on a target nitrogen level corresponding to the particular crop and / or a particular sub-region of the crop. Over time, as the system continues to collect additional data for the crop, the system can continue to refine the target nitrogen level so that one or more users associated with the crop can apply nitrogen to the soil at specific times during the growing season and / or in specific sub-regions of the crop based on target nitrogen levels defined for that particular crop and / or sub-region of the crop and configured to produce healthy plants in the crop.

[0112] Additionally, during this first growing season, the system can: obtain an application rate of nitrogen specified by a user associated with the crop (e.g., a farmer, agronomist), such as applied to the soil on the crop and / or a specific sub-area of ​​the crop; and characterize the uptake of nitrogen by plants in the crop (e.g., plants in a specific sub-area of ​​the crop and / or generally all plants across the crop) based on a difference between the application rate of nitrogen and the amount of nitrogen detected at sensitive plants in the crop (e.g., based on a fluorescent signal expressed by the sensitive plants).

[0113] For example, the system may obtain an initial nitrogen application amount applied to the soil in the crop at an initial time corresponding to the start of the growing season. Then, at the first time, the system may: obtain a first image of the crop recorded by a satellite; detect a first fluorescent signal expressed by a sensitive plant in a first sub-area of ​​the crop; detect a second fluorescent signal expressed by a sensitive plant in a second sub-area of ​​the crop; interpret a first amount of nitrogen consumed by the plants in the first sub-area (e.g., from the soil) based on the first fluorescent signal; and interpret a second amount of nitrogen consumed by the plants in the second sub-area based on the second fluorescent signal. Then, for the first sub-area, the system may: calculate a first ratio of the first nitrogen amount to the initial nitrogen application amount; calculate a duration between the first time and the initial time; and characterize a first nitrogen absorption (e.g., a nitrogen consumption rate) based on the first ratio and the duration. Similarly, for the second sub-area, the system may: calculate a second ratio of the second nitrogen amount to the initial nitrogen application amount; and characterize a second nitrogen absorption (e.g., a nitrogen consumption rate) based on the second ratio and the duration. Thus, prior to subsequently applying nitrogen to the soil in a crop, the system can: derive a first nitrogen application rate for a first sub-area of ​​the crop, the first nitrogen application rate being predicted to produce nitrogen uptake corresponding to a target nitrogen content in plants in the first sub-area; derive a second nitrogen application rate for a second sub-area of ​​the crop, the second nitrogen application rate being predicted to produce nitrogen uptake corresponding to the target nitrogen content in plants in the second sub-area; generate a prompt to apply the first nitrogen application rate and the second nitrogen application rate in the first sub-area and the second sub-area, respectively, at a second time (e.g., during a first growing season); and send the prompt to a user or user group associated with the crop.

[0114] 6.2 Characterization plant Health status The system can characterize plant health in a particular crop based on a set of target plant conditions defined for that crop.

[0115] In particular, in this embodiment, the system can characterize plant health based on the differences between a set of current plant conditions of the crop (such as derived from fluorescent signals expressed by sensitive plants in the crop and detected in an image of the crop) and a set of target plant conditions defined for the crop.

[0116] For example, a crop may include sensitive plants that are configured to signal nitrogen absorption in the sensitive plants (e.g., in the crop). In particular, each sensitive plant in a set of sensitive plants located in the crop may be configured to output a fluorescence signal within a target wavelength range that represents the amount of nitrogen consumed by the sensitive plant (e.g., from the soil). In this example, during a first time period, the system may: derive a target nitrogen content of the soil in the crop (such as a target nitrogen dosage that defines the frequency of nitrogen application and the amount of nitrogen applied at each application), the target nitrogen content being configured to produce a plant "healthy" plant in the particular crop, such as characterized by a health score exceeding a threshold health score; and store the target nitrogen content in a crop profile generated for the crop.

[0117] Then, during a second time period following the first time period, the system can: acquire an image feed of sensitive plants in the crop recorded during the second time period by an imaging system (e.g., a satellite, an optical sensor mounted on a drone, a tractor, or a pole mounted in the crop, or a camera on a mobile device), the imaging system configured to capture fluorescence signals output by the sensitive plants, the sensitive plants configured to output fluorescence signals within a target wavelength in response to various plant stressors; extract a first intensity of the fluorescence signal within the target wavelength range (e.g., a composite fluorescence signal output by a group of sensitive plants); and predict a current nitrogen level in the plants in the crop based on the first intensity of the fluorescence signal.

[0118] In the foregoing example, the system may then: obtain a target nitrogen content stored in a crop profile defined for the particular crop; characterize a difference between the current nitrogen content and the target nitrogen content; and based on the difference, characterize the health of the plants in the crop (or "plant health"). For example, the system may estimate a health score for the plants in the crop, such as represented by a percentage between 0% and 100%, a score between 0 and 10, a rating of poor health, medium health, good health, or excellent health, etc. In one example, in response to the difference exceeding an upper threshold difference defined for the plants in the particular crop, the system may estimate a relatively low health score for the plants in the crop (e.g., "5%", "1 / 10", poor health). Alternatively, in response to the difference falling below the upper threshold difference and exceeding the lower threshold difference, the system may estimate a relatively medium health score for the plants in the crop (e.g., "50%", 5 / 10, medium health). Alternatively, in response to the difference falling below the lower threshold difference, the system may estimate a relatively high health score for the plants in the crop (e.g., "95%", 9 / 10, excellent health).

[0119] 6.2.1 Health situation Model In one variation, the system may derive a plant health model configured to take in a set of current conditions detected for a particular sensitive plant or group of sensitive plants and output a prediction of the current plant health of the particular sensitive plant or group of sensitive plants.

[0120] For example, during a first growing season for a particular crop, the system may: track a first set of plant conditions at a first set of sensitive plants located in a first sub-area of ​​the crop; track a second set of plant conditions at a second set of sensitive plants located in a second sub-area of ​​the crop; obtain a first set of plant surveys completed by one or more users associated with the crop, the first set of plant surveys indicating the health of plants in the first sub-area throughout the first growing season; obtain a second set of surveys completed by one or more users associated with the crop, the second set of surveys indicating the health of plants in the second sub-area throughout the first growing season; and / or obtain a first set of crop data collected for the crop during the first growing season, such as including weather data, crop yield data (e.g., crop yield data for the entire crop and / or for a sub-area of ​​the crop), crop treatment data (e.g., crop treatment data for the entire crop and / or for a sub-area of ​​the crop), etc.

[0121] The system can then: derive a first health status model based on the first set of plant conditions, the first set of plant surveys, and the first set of crop data, the first health status model being configured to predict the health status of plants in the first sub-area of ​​the crop based on the plant conditions at sensitive plants in the first sub-area; and derive a first health status model based on the second set of plant conditions, the second set of plant surveys, and the first set of crop data, the first health status model being configured to predict the health status of plants in the first sub-area of ​​the crop based on the plant conditions at sensitive plants in the first sub-area.

[0122] Then, during a subsequent growing season, the system may: acquire a first image depicting plants in a first sub-region of the crop; interpret a first set of plant conditions based on the fluorescence signals extracted from the first image; and characterize the health of the plants in the first sub-region, such as by a first plant health score, based on the first set of plant conditions and a first health model. In addition, the system may: acquire a second image depicting plants in a second sub-region of the crop; interpret a second set of plant conditions based on the fluorescence signals extracted from the second image; and characterize the health of the plants in the second sub-region, such as by a second plant health score, based on the second set of plant conditions and a second health model. In this example, the system may then select a specific treatment pathway for implementation in each sub-region of the crop, thereby enabling the treatment of the plants in each sub-region to be customized based on changes in the health of the plants in those sub-regions.

[0123] In one example, the system can derive a health status model that is configured to output a health status score representing the health status of plants in a particular sub-area of ​​a crop based on a magnitude of nitrogen depletion detected in sensitive plants in the particular sub-area, a magnitude of water depletion detected in sensitive plants in the particular sub-area, a magnitude of fungal pressure detected in the particular sub-area, and / or a magnitude of insect pressure detected in the particular sub-area.

[0124] 6.3 Targeted treatment The system can use the difference between a set of target conditions defined for a crop and the (currently) detected conditions in that crop to recommend targeted treatments for plants in that crop.

[0125] In particular, the system can selectively recommend treatments (such as to a farmer or agronomist associated with a crop) that are configured to shift current conditions within the crop toward a set of target conditions defined for the crop and / or for a particular subset of plants within the crop (e.g., in a particular sub-region).

[0126] Thus, the system can: acquire an image or images of sensitive plants in a crop; extract a fluorescent signal representing a first condition of the sensitive plants (such as an amount of a fungal stressor present at the sensitive plants or an amount of nitrogen consumed by the sensitive plants) using a reporter model defined for the sensitive plants; acquire a target condition defined for the plants in the crop and corresponding to the first condition, such as a threshold amount (e.g., a maximum amount) of a fungal stressor that the plants in the crop can tolerate or a target nitrogen content (e.g., within a target range) configured to maximize plant health or growth; characterize a difference between the first condition and the target condition; and based on the difference, select a treatment pathway for the plants in the crop that is configured to adjust the first condition toward the target condition. The system can then prompt a user associated with the crop to implement the treatment pathway and / or automatically trigger implementation of the treatment pathway, such as via an automated treatment system.

[0127] Furthermore, by utilizing signals generated by sensitive plants distributed throughout the crop, the system can selectively recommend different treatment types and / or treatment dosages in different regions within that particular crop. Thus, the system can enable targeted treatment of plants within a particular crop and / or geographic region (e.g., across multiple crops) based on conditions within each of these different regions.

[0128] For example, the system may: obtain an image feed of a sensitive plant population, the sensitive plant population including a first set of sensitive plants sown in a first sub-region of a crop and a second set of sensitive plants arranged in a second sub-region of the crop; interpret a first set of conditions at the plants in the first sub-region based on a first subset of features extracted from a first image in the image feed (such as fluorescence intensity within a first wavelength range); obtain a set of target conditions defined for the plants in the crop; characterize a first difference between the first set of conditions and the set of target conditions; and select a first treatment route for application in the plants in the first sub-region in response to the first difference exceeding a threshold difference. The system may also: interpret a second set of conditions at the plants in the second sub-region based on a second subset of features extracted from the first image (such as fluorescence intensity within a first wavelength range); characterize a second difference between the second set of conditions and the set of target conditions; and select a second treatment route for application in the plants in the second sub-region in response to the second difference exceeding a threshold difference. The system may then: generate a notification including a prompt to implement the first treatment route in the first sub-region and the second treatment route in the second sub-region; and send the prompt to a farmer associated with the crop.

[0129] 6.3.1 Processing diagram In one variation, the system may derive a treatment map for a particular crop based on a set of target conditions defined for the crop. In particular, in this variation, the system may integrate a treatment map based on treatment paths selected for various sub-regions of the crop, the treatment map specifying the location and magnitude (e.g., frequency and / or amount) of application of each treatment path selected for the crop.

[0130] For example, the system may: obtain an image of a crop recorded by a satellite at a first time, the image depicting a set of fluorescent signals expressed by sensitive plants in the crop; interpret a first nitrogen content in plants in a first sub-region of the crop based on a first fluorescent signal in the set of fluorescent signals; interpret a second nitrogen content in plants in a second sub-region of the crop based on a second fluorescent signal in the set of fluorescent signals; and interpret a third nitrogen content in plants in a third sub-region of the crop based on a third fluorescent signal in the set of fluorescent signals. Then, for the first sub-region of the crop, the system may: obtain a first target nitrogen content corresponding to plants in the first sub-region in a set of target nitrogen contents derived for the crop; characterize the difference between the first nitrogen content and the first target nitrogen content; and derive a first nitrogen application rate for application to soil in the first sub-region at a second time after the first time based on the difference. The system may similarly repeat the process for the second sub-region and the third sub-region to derive a second nitrogen application rate for application to soil in the second sub-region at a second time and a third nitrogen application rate for application to soil in the third sub-region at a second time. Then, the system can: export a treatment map for implementation at approximately a second time, the treatment map indicating application of a first nitrogen application rate, a second nitrogen application rate, and a third nitrogen application rate in a first sub-region, a second sub-region, and a third sub-region of the crop, respectively; and send the treatment map to a user for execution in the crop at approximately the second time. Additionally and / or alternatively, in this example, the system can upload the treatment map to an autonomous vehicle (e.g., an autonomous tractor or drone) that is configured to autonomously traverse the crop and apply nitrogen to soil in the crop according to the treatment map.

[0131] In one embodiment, the system can derive a treatment map that specifies the type of treatment and the treatment dosage (e.g., the amount and / or duration of the treatment) for application in a specific area of ​​the crop and / or within a geographic area. For example, the system can: interpret a first pressure of a first stressor in a first area of ​​the crop based on features extracted from a first portion of a first image depicting sensitive plants in the first area; interpret a second pressure of the first stressor in a second area of ​​the crop based on features extracted from a second portion of the first image depicting sensitive plants in the second area; select a first mitigation technique (e.g., applying a pesticide or fungicide, watering the plant, fertilizing the plant), the first mitigation technique configured to mitigate the pressure of the first stressor in the crop; define a dosage gradient for applying the first mitigation technique on the crop based on the first pressure and the second pressure; and generate a treatment map based on the first mitigation technique and the dosage gradient. For example, the system can generate a treatment map that defines: a first dosage of a first mitigation action in a first area of ​​the crop; and a second dosage of the first mitigation action in a second area of ​​the crop that is less than the first dosage in response to the second pressure dropping below the first pressure.

[0132] 6.4 Crops Stress resistance In one variation, the system can characterize crop resistance of a particular crop to a set of plant stressors (e.g., abiotic stressors and / or biotic stressors), such as drought, overheating, nutrient deficiency (e.g., nitrogen deficiency, phosphorus deficiency), overwatering, salinity, soil pH, soil contamination, insects, fungi, weed growth, etc.

[0133] In particular, in this embodiment, the system can characterize the resistance of the crop to a particular stressor in the set of plant stressors based on a target plant condition associated with the particular stressor defined for the crop. For example, the system can quantify the resistance of plants in the crop to nitrogen deficiency as an inverse function of a target nitrogen content defined for the plants in the crop. More specifically, in this example, if the target nitrogen content defined for the plants in the crop is relatively low, the system can quantify the resistance of these plants to nitrogen deficiency as relatively high. Alternatively, in this example, if the target nitrogen content defined for the plants in the crop is relatively high, the system can quantify the resistance of these plants to nitrogen deficiency as relatively low.

[0134] Additionally and / or alternatively, in another embodiment, the system can characterize the resistance of the crop to a particular stressor in the set of plant stressors based on the sensitivity of the plants in the crop to deviations from target plant conditions associated with the particular stressor. For example, the system can: interpret the sensitivity of the plants in the crop to nitrogen deficiency (such as below a target nitrogen content defined for the plants in the crop) based on observed changes in plant health; and quantify the resistance of the plants in the crop to nitrogen deficiency as an inverse function of the sensitivity. The system can similarly repeat this process for each stressor in the set of plant stressors to characterize the resistance of the crop or plants in the particular crop to the set of plant stressors.

[0135] The systems and methods described herein may be at least partially embodied and / or implemented as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions may be executed by a computer-executable component integrated with an application, applet, host, server, network, website, communication service, communication interface, hardware / firmware / software element of a user computer or mobile device, a wristwatch, a smart phone, or any suitable combination thereof. Other systems and methods of embodiments may be at least partially embodied and / or implemented as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions may be executed by a computer-executable component integrated with a computer-executable component integrated with the above-mentioned types of devices and networks. The computer-readable medium may be stored on any suitable computer-readable medium, such as RAM, ROM, flash memory, EEPROM, optical device (CD or DVD), hard drive, floppy disk drive, or any suitable device. The computer-executable component may be a processor, but any suitable dedicated hardware device may (optionally or additionally) execute the instructions.

[0136] As those skilled in the art will recognize from the previous detailed description and from the accompanying drawings and claims, modifications and variations may be made to the embodiments of the invention without departing from the scope of the invention as defined in the appended claims.

Claims

1. A method comprising: - acquiring an image feed of sensitive plants of a sensitive plant type sown in a set of agricultural fields within a geographic area, the image feed being recorded during a first time period, the sensitive plants of the sensitive plant type being configured to signal the presence of a set of stressors at the sensitive plants; - explaining a first stress of a first stressor of the set of stressors in a first sub-region within the geographic region based on features extracted from a first subset of images of the image feed depicting sensitive plants in the first sub-region; - interpreting a second pressure of the first stressor in a second sub-region within the geographical area based on features extracted from a second subset of images in the image feed depicting sensitive plants in the second sub-region; - deriving a first pressure map for the geographical area based on the first pressure and the second pressure, the first pressure map indicating the presence of the set of stressors in the geographical area during the first time period; - obtaining a yield model that relates stress of the set of stressors within the geographic region to crop yield of crops of a set of crop types in a target region; and - predicting a first crop yield of a crop of a first crop type from the group of crop types in the geographical area based on the first pressure map and the yield model.

2. The method according to claim 1: -in, predicting the first crop yield of a crop of the first crop type based on the first pressure map and the yield model includes predicting the first crop yield of the crop of the first crop type based on a first subset of features extracted from a region of the first pressure map corresponding to the crop of the first crop type and the yield model; and - Also includes: - predicting a second crop yield of crops of a second crop type in the set of crop types in the geographic area based on a second subset of features extracted from an area of ​​the first pressure map corresponding to crops of the second crop type in the set of crop types and the yield model; and - generating a first yield profile for the geographic area in a set of yield profiles based on the first crop yield of crops of the first crop type and the second crop yield of crops of the second crop type.

3. The method according to claim 1: -in, Deriving the first pressure map includes: - predicting a third pressure of the first stressor at a third sub-region within the geographic region based on the first pressure of the first stressor in the first sub-region and the second pressure of the first stressor in the second sub-region; and - deriving the first pressure map for the geographic area based on the first pressure, the second pressure and the third pressure; and - wherein predicting the first crop yield of crops of the first crop type in the geographic area comprises predicting the first crop yield of crops of the first crop type in the geographic area comprising: a first crop of the first crop type which is located in the first sub-area and comprises a first group of sensitive plants of the sensitive plant type; a second crop of said first crop type which is located in said second sub-area and comprises a second group of sensitive plants of said sensitive plant type; and - A third crop of the first crop type located in the third sub-area.

4. The method according to claim 1: - Also includes: - interpreting a third pressure of a second stressor from the set of stressors in the first sub-region based on features extracted from the first subset of images; and - interpreting a fourth pressure of the second stressor in the second sub-region of the set of stressors based on features extracted from the second subset of images; and - wherein deriving the first pressure map of the geographical area based on the first pressure and the second pressure comprises deriving the first pressure map of the geographical area based on the first pressure of the first stressor, the second pressure of the first stressor, the third pressure of the second stressor and the fourth pressure of the second stressor.

5. The method according to claim 4: -in, Interpreting the first pressure of the first stressor in the first sub-region based on features extracted from the first image subset comprises: -- extracting a first set of fluorescence measurements within a first wavelength range from said first subset of images; and - interpreting the first pressure of the first stressor in the first sub-region based on the first set of fluorescence measurements; and - wherein interpreting the third pressure of the second stressor in the first sub-region based on features extracted from the first image subset comprises: - extracting a second set of fluorescence measurements in a second wavelength range from said first subset of images; and - Interpreting the third pressure of the second stressor in the first sub-region based on the second set of fluorescence measurements.

6. The method of claim 1 , further comprising during the initial time period: - for each growth phase in a series of growth phases within said initial time period: -- recording a time series of pressure data, the time series of pressure data representing the pressure of the set of stressors in the geographic area throughout the growing period; -- record the final crop yield of crops of said crop type within said geographic area; and --storing the time series of pressure data and the final crop yield in a growing period grouping in a series of growing period groupings; and - deriving a model relating stress of the set of stressors within the geographic region to crop yield of the crop type within the geographic region based on the series of growing period groupings.

7. The method according to claim 1: - Also includes: obtaining a time series of weather data recorded for the geographic area during the first time period; and -Wherein, predicting the first crop yield based on the first pressure map and the yield model includes predicting the first crop yield based on the first pressure map, the time series of the weather data and the yield model.

8. The method according to claim 1, further comprising: - predicting a third pressure of the first stressor in the first sub-area at a second time after the first time based on the first pressure map; - predicting a fourth pressure of the first stressor in the second sub-area at the second time based on the first pressure map; - deriving a second pressure map for the geographical area based on the third pressure of the first stressor and the fourth pressure of the first stressor; - generating a treatment map for implementation at the second time based on the second pressure map; and - sending said processing map to a group of users associated with crops in said geographical area.

9. The method according to claim 8: -in, Generating the processing graph includes: - selecting a first mitigation technology from a group of mitigation technologies, the first mitigation technology configured to mitigate stress of the first stressor in the geographic area; and -- defining a dose gradient based on the first pressure map, the dose gradient comprising an array of doses for administering the first mitigation technique across the geographic region; and wherein sending the processing map to the group of users associated with crops in the geographical area comprises: - sending the processing graph to a first subset of users of the group of users associated with applying the first mitigation technique within the geographic area; and - Sending the process graph to a second subset of users of the group of users associated with the manufacture of chemicals associated with the first mitigation technique.

10. The method according to claim 1, further comprising: - obtaining a target pressure range defined for the pressure of the first stressor in the first sub-area; - characterizing a difference between the first pressure and the target pressure range; and In response to the difference exceeding a threshold difference: - selecting a first relief action from a set of relief actions, wherein the first relief action is configured to relieve the pressure of the first stressor; --selecting a first dose of the mitigating action based on the difference; -- generating a prompt to perform the first mitigation action at the first dosage in crops of the crop type within the first sub-area; and --Sending the prompt to a group of users associated with crops in the first sub-area.

11. The method according to claim 1, further comprising: - at a second time after the first time, in response to harvesting a crop of the crop type within the geographic area, obtaining a final crop yield of a crop of the crop type harvested during the growing period; - obtaining a time series of pressure maps derived for said geographical area during said growing period, said time series of pressure maps comprising said first pressure map; and - Modifying the yield model based on the time series of the pressure map and the final crop yield.

12. The method according to claim 1, further comprising: -Based on the first pressure map: - selecting a first relief action from a set of relief actions, wherein the first relief action is configured to relieve the pressure of the first stressor; -- generating a first prompt to implement the first mitigation action in crops of the one crop type within the geographic area during a second time period after the first time period; and --sending said first prompt to a group of users associated with crops of said crop type in said geographic area; - interpreting a third pressure of the first stressor in the first sub-area based on features extracted from a third subset of images in the image feed depicting sensitive plants in the first sub-area; - interpreting a fourth pressure of the first stressor in the second sub-area based on features extracted from a fourth subset of images in the image feed depicting sensitive plants in the second sub-area; - characterizing a first difference between said third pressure and said first pressure in said first sub-area; - characterizing a second difference between the second pressure and the fourth pressure in the second sub-area; In response to the first difference exceeding a threshold difference: -- generating a second prompt to implement the first mitigation action in crops of the crop type within the first sub-area during a third time period after the second time period; and - sending the second prompt to a first subset of users of the group of users who are associated with crops of the crop type in the first sub-area; and In response to the second difference falling below the threshold difference: - selecting a second relief action from the set of relief actions to replace the first relief action, and the second relief action is configured to relieve the pressure of the first stressor; -- generating a third prompt to implement the second mitigation action in crops of the crop type within the second sub-area during the third time period; and - Sending the third prompt to a second subset of users of the group of users who are associated with crops of the crop type in the second sub-area.

13. The method according to claim 1: -in, Acquiring the image feed of a sensitive plant includes acquiring the image feed of a sensitive plant of the sensitive plant type, the sensitive plant being configured as: --selectively outputting fluorescent signals across a set of wavelength ranges based on the set of stressors; and -- outputting a baseline fluorescence signal within a target wavelength range associated with a crop of the crop type, the wavelengths within the target wavelength range being outside the set of wavelength ranges; - wherein interpreting the first pressure of the first stressor in the first sub-region based on features extracted from the first image subset comprises: - identifying a first group of crops of said crop type within said first sub-area based on detection of said baseline fluorescent signal within said first image subset; and -- interpreting the first stress of the first stressor in the first group of crops based on detection of fluorescence signals across the set of wavelength ranges within the first subset of images; and - wherein interpreting the second pressure of the first stressor in the second sub-region based on features extracted from the second image subset comprises: - identifying a second group of crops of said crop type within said second sub-area based on detection of said baseline fluorescent signal within said second image subset; and - interpreting the second stress of the first stressor in the second group of crops based on detection of fluorescence signals across the set of wavelength ranges within the second subset of images.

14. The method according to claim 1, wherein: Acquiring the image feed of sensitive plants of the sensitive plant type includes acquiring the image feed of sensitive plants of the sensitive plant type including: - a first promoter of a group of promoters, the first promoter being configured to be activated in response to pressure from the first stressor; and - a first reporter of a set of reporters, said first reporter being associated with said first promoter and being configured to express a detectable fluorescent signal in response to activation of said first promoter and corresponding to stress by said first stressor.

15. The method according to claim 1: -in, Interpreting the first pressure of the first stressor includes interpreting a first magnitude of nitrogen uptake in plants in the first sub-region; - wherein interpreting said second pressure of said first stressor comprises interpreting a second magnitude of nitrogen uptake in plants in said second sub-region; and - Among them, the forecast total crop production includes: --obtaining a target nitrogen uptake defined for plants in the geographic region; --predicting a first health score for plants in the first sub-region based on the first magnitude of the nitrogen uptake and the target nitrogen uptake; - predicting a second health score for plants in the second sub-region based on the second magnitude of the nitrogen uptake and the target nitrogen uptake; and --Predicting the total crop yield for the geographic area based on the first health score, the second health score, and the yield model.

16. A method comprising: - acquiring an image feed of a population of sensitive plants of a crop type sown in an agricultural field, the image feed recorded by an aerial sensor during a first time period, the population of sensitive plants being configured to signal the presence of a set of conditions and comprising a first set of sensitive plants arranged in a first area of ​​the agricultural field; - interpreting a first set of conditions at plants in said first area based on features extracted from a first subset of images in said image feed depicting sensitive plants in said first area; - obtaining a set of target conditions defined for plants in said first area of ​​said agricultural field; - predicting a first health score for plants in the first area based on a first difference between the first set of conditions and the set of target conditions; - based on the first health score, selecting a first treatment pathway among a set of treatment pathways for plants in the first region, the first treatment pathway configured to drive a condition of plants in the agricultural field toward the set of target conditions; - generating a prompt to implement the first processing path during a second time period after the first time period; and - Sending said reminder to users associated with said farmland.

17. The method according to claim 16: -in, Acquiring the image feed of the sensitive plant population including the first group of sensitive plants in the first area includes acquiring the image feed of the sensitive plant population including the first group of sensitive plants arranged in the first area and a second group of sensitive plants arranged in a second area of ​​the agricultural field; - Also includes: - interpreting a second set of conditions at plants in the second area based on a second set of signals detected in a second subset of images in the image feed depicting sensitive plants in the second area; -- obtaining a second set of target conditions defined for plants in the second area; - predicting a second health score for plants in the second area of ​​the farmland based on a second difference between the second set of conditions and the second set of target conditions; and - based on the second health score, selecting a second treatment path for plants in the second zone; and - wherein generating the prompt to implement the first processing path during the second time period comprises generating the prompt to implement the first processing path in the first area and the second processing path in the second area during the second time period.

18. The method according to claim 17: -in, Selecting the first treatment pathway for plants in the first region based on the first health score includes: selecting the first treatment pathway for plants in the first region in response to the first health score falling below a threshold health score; and -Wherein, selecting the second treatment path for the plants in the second sub-area based on the second health score includes: in response to the second health score exceeding the threshold health score, selecting the second treatment path for the plants in the first sub-area, the second treatment path being configured to maintain the second set of plant conditions in the plants in the second sub-area.

19. The method according to claim 16: -in, Acquiring the image feed of the sensitive plant population including the first group of sensitive plants in the first sub-area includes acquiring the image feed of the sensitive plant population including the first group of sensitive plants arranged in the first area and a second group of sensitive plants arranged in a second area of ​​the agricultural field; - wherein interpreting the first set of conditions at plants in the first region comprises interpreting a first pressure of a first stressor in a set of stressors in plants in the first region and a second pressure of a second stressor in the set of stressors in plants in the first region; and - Also includes: interpreting a second set of conditions at plants in the second region based on features extracted from a second subset of images in the image feed depicting susceptible plants in the second region, the second set of conditions comprising a third stress of the first stressor in plants in the second region; and a fourth stress of the second stressor in plants in the second region; --obtaining a second set of target conditions defined for plants in the second area; - predicting a second health score for plants in the second area of ​​the agricultural field based on a second difference between the second set of conditions and the second set of target conditions; --obtaining a yield model that relates plant health status of crops of the crop type in the farmland to crop yield; and --Predicting a first crop yield of a crop of the crop type based on the first health score, the second health score, and the yield model.

20. A method comprising: - acquiring an image feed of a sensitive plant population in a crop of a crop type sown in a target area and configured to signal the presence of a set of stressors at the sensitive plants, the image feed being captured by an optical sensor during a first time period; - interpreting a first time series of stress data for plants in a first sub-region of a set of sub-regions of the target area based on features extracted from a first subset of images in the image feed captured during a first time period, the first time series of stress data representing changes in stress of a set of stressors at plants in the first sub-region during the first time period; - interpreting a second time series of stress data for plants in a second sub-region of the set of sub-regions of the target area based on features extracted from a second subset of images in the image feed captured during the first time period, the second time series of stress data representing changes in stress of the set of stressors at plants in the second sub-region during the first time period; - obtaining a yield model that relates stress of the set of stressors in the target area to crop yield of crops of the one crop type in the target area; and -Based on the first time series of the pressure data, the second time series of the pressure data and the yield model, predicting the first crop yield of a crop of the one crop type among the group of crop types in the target area during a target harvest time period after the first time period.

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