Abstract: Disclosed are various embodiments for using artificial intelligence to monitor harvest losses of combine harvesters. Images can be periodically captured from a ground-facing camera mounted to a combine harvester. An amount of gleanings can be counted in the image. An estimated amount of harvest loss is then calculated based at least in part on the amount of gleanings. The estimated amount of the harvest loss can then be displayed to a user or can be used as the basis for automatically adjusting the operation of the combine harvester.

Abstract: Disclosed are various embodiments for using artificial intelligence to monitor harvest losses of combine harvesters. Images can be periodically captured from a ground-facing camera mounted to a combine harvester. An amount of gleanings can be counted in the image. An estimated amount of harvest loss is then calculated based at least in part on the amount of gleanings. The estimated amount of the harvest loss can then be displayed to a user or can be used as the basis for automatically adjusting the operation of the combine harvester.

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.

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.

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.

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.

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.

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.