# US 2019/0171880 A1 · Automated diagnosis and treatment of crop infestations > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0171880 A1 - Document ID: US20190171880A1 - Status: Published application - Kind: A1 - Application number: 16/254,963 - Filed: 2019-01-23 - Published: 2019-06-06 - Earliest claimed: 2017-03-02 - Assignees: Farmwave, LLC - Applicants: Basecamp Networks, LLC - Inventors: Craig Ganssle - Web page: https://patentgrep.com/patent/US20190171880A1 ## CPC - G06V 20/188: {Vegetation} - A01G 22/00: Cultivation of specific crops or plants not otherwise provided for - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - A01G: HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING (picking of fruits, vegetables, hops or the like A01D46/00; propagating unicellular algae C12N1/12) ## Abstract Disclosed are various embodiments for automating the diagnosis of crop infestations and estimating crop yields. In some embodiments, a field report that includes an image of a crop and a location of a corresponding field is received from a computing device. The image of the crop is processed using computer-vision techniques to identify a pathogen affecting the crop. A biocide to apply to the crop to treat the pathogen is then identified. In some embodiments, the yield of the crop can also be estimated. In such embodiments, a field report that includes images of plants in a field and an identifier of the field is received. A computer-vision technique is applied to each image to determine an individual yield of each plant in an image. An estimate of the yield of the crop is then calculated based on the individual yields of the plants and the size of the field. ## Claims ### Claim 1 A system, comprising: a first computing device comprising a processor and a memory; and machine readable instructions stored in the memory that, when executed by the processor, cause the first computing device to at least: receive a field report from a second computing device, the field report comprising a plurality of images of a corresponding plurality of plants in a crop and an identifier of a respective field; apply a computer-vision technique to each image in the plurality of images to determine an individual yield for each of the corresponding plants in the crop; determine a size of a corresponding field based on the identifier of the corresponding field; and calculate an estimated crop yield based at least in part on the individual yield for each of the corresponding plants in the crop and the size of the corresponding field. ### Claim 2 (depends on 1) The system of claim 1, wherein the machine readable instructions further cause the first computing device to at least: apply the computer-vision technique to each image in the plurality of images to determine a type of the crop; and calculate the estimated crop yield based at least in part on the type of the crop. ### Claim 3 (depends on 1) The system of claim 1, wherein the machine readable instructions stored in the memory that cause the first computing device to at least apply the computer-vision technique to each image in the plurality of images to determine the individual yield for each of the corresponding plants in the crop further causes the first computing device to at least: apply a first object-recognition technique to each image in the plurality of images to determine a type of the crop; select a second object-recognition technique based on the type of the crop; and apply the second object-recognition technique each image in the plurality of images to determine the individual yield for each of the corresponding plants in the crop. ### Claim 4 (depends on 1) The system of claim 1, wherein the estimated crop yield is further based at least in part on planting data associated with the corresponding field. ### Claim 5 (depends on 1) The system of claim 1, wherein the second computing device comprises a wearable computing device. ### Claim 6 (depends on 1) The system of claim 1, wherein the second computing device comprises a mobile computing device. ### Claim 7 A non-transitory, computer-readable medium comprising machine-readable instructions that, when executed by a processor of a first computing device, cause the first computing device to at least: receive a field report from a second computing device, the field report comprising a plurality of images of a corresponding plurality of plants in a crop and an identifier of a respective field; apply a computer-vision technique to each image in the plurality of images to determine an individual yield for each of the corresponding plants in the crop; determine a size of a corresponding field based on the identifier of the corresponding field; and calculate an estimated crop yield based at least in part on the individual yield for each of the corresponding plants in the crop and the size of the corresponding field. ### Claim 8 (depends on 7) The non-transitory, computer-readable medium comprising machine-readable of claim 7, wherein the machine readable instructions further cause the first computing device to at least: apply the computer-vision technique to each image in the plurality of images to determine a type of the crop; and calculate the estimated crop yield based at least in part on the type of the crop. ### Claim 9 (depends on 7) The non-transitory, computer-readable medium comprising machine-readable of claim 7, wherein the machine readable instructions that cause the first computing device to at least apply the computer-vision technique to each image in the plurality of images to determine the individual yield for each of the corresponding plants in the crop further causes the first computing device to at least: apply a first object-recognition technique to each image in the plurality of images to determine a type of the crop; select a second object-recognition technique based on the type of the crop; and apply the second object-recognition technique each image in the plurality of images to determine the individual yield for each of the corresponding plants in the crop. ### Claim 10 (depends on 7) The non-transitory, computer-readable medium comprising machine-readable of claim 7, wherein the estimated crop yield is further based at least in part on planting data associated with the corresponding field. ### Claim 11 (depends on 7) The non-transitory, computer-readable medium comprising machine-readable of claim 7, wherein the second computing device comprises a wearable computing device. ### Claim 12 (depends on 7) The non-transitory, computer-readable medium comprising machine-readable of claim 7, wherein the second computing device comprises a mobile computing device. ### Claim 13 A method, comprising: receiving, with a first computing device, a field report from a second computing device, the field report comprising a plurality of images of a corresponding plurality of plants in a crop and an identifier of a respective field; applying, with the first computing device, a computer-vision technique to each image in the plurality of images to determine an individual yield for each of the corresponding plants in the crop; determining, with the first computing device, a size of a corresponding field based on the identifier of the corresponding field; and calculating, with the first computing device, an estimated crop yield based at least in part on the individual yield for each of the corresponding plants in the crop and the size of the corresponding field. ### Claim 14 (depends on 13) The method of claim 13, further comprising: applying, with the first computing device, the computer-vision technique to each image in the plurality of images to determine a type of the crop; and calculating, with the first computing device, the estimated crop yield based at least in part on the type of the crop. ### Claim 15 (depends on 13) The method of claim 13, wherein applying the computer-vision technique to each image in the plurality of images to determine the individual yield for each of the corresponding plants in the crop further comprises: applying, with the first computing device, a first object-recognition technique to each image in the plurality of images to determine a type of the crop; selecting, with the first computing device a second object-recognition technique based on the type of the crop; and applying, with the first computing device, the second object-recognition technique each image in the plurality of images to determine the individual yield for each of the corresponding plants in the crop. ### Claim 16 (depends on 13) The method of claim 13, wherein the estimated crop yield is further based at least in part on planting data associated with the corresponding field. ### Claim 17 (depends on 13) The method of claim 13, wherein the second computing device comprises a wearable computing device. ### Claim 18 (depends on 13) The method of claim 13, wherein the second computing device comprises a mobile computing device.