System and method for predicting crop yield
A device includes an image data receiving component, a vegetation index generation component, a crop data receiving component, a masking component and a multivariate regression component. The image data receiving component receives image data of a geographic region. The vegetation index generation component generates an array of vegetation indices based on the received image data, and includes a plurality of vegetation index generating components, each operable to generate a respective individual vegetation index based on the received image data. The crop data receiving component receives crop data associated with the geographic region. The masking component generates a masked vegetation index based on the array of vegetation indices and the received crop data. The multivariate regression component generates a crop parameter based on the masked vegetation index.
1. A method, comprising:
receiving, with a computer, image data of a geographic region, the image data including pixels;
generating, with the computer, an array of vegetation indices based on analyzing the received image data utilizing a plurality of vegetation index generating components, each operable to generate a respective individual vegetation index based on the received image data, wherein each vegetation index in the array of vegetation indices is associated with a respective weighting factor;
receiving, with the computer, crop data comprising training data for classifying one or more of the pixels as a type of crop;
generating, with the computer, a masked vegetation index based on the array of vegetation indices and the received crop data, such that the pixels are associated with vegetation index data from each vegetation index in the array of vegetation indices and such that the pixels are associated with a classification as a type of crop;
receiving, with the computer, historical crop yield data; and
generating, with the computer, utilizing multivariate regression, a predicted crop yield based on the masked vegetation index and the historical crop yield data, wherein the multivariate regression includes utilizing the weighting factors of the array of vegetation indices and includes modifying the weighting factors of the array of vegetation indices based on past accuracy of results of individual ones of the array of vegetation indices, such that accuracy of the predicted crop yield in relation to the historical crop yield data is increased.
2. The method of claim 1 , wherein said generating, with the computer, an array of vegetation indices based on the received image data comprises generating the array of vegetation indices as an array of normalized difference vegetation indices.
3. The method of claim 1 , wherein said receiving, with the computer, image data of the geographic region comprises receiving multiband image data of a geographic region as RGB and near infrared image data of the geographic region.
4. The method of claim 1 , claim 1 , further comprising:
receiving, with the computer, demographic data,
wherein said generating, with the computer, the predicted crop yield based on the masked vegetation index comprises generating the predicted crop yield additionally based on the demographic data.
5. The method of claim 1 , claim 1 , further comprising:
receiving, with the computer, weather data,
wherein said generating, with the computer, the predicted crop yield based on the masked vegetation index comprises generating the predicted crop yield additionally based on the weather data.
6. The method of claim 1 , wherein the predicted crop yield is a first predicted crop yield and the historical crop yield data is first historical crop yield data, and further comprising generating, with the computer, a second predicted crop yield based on second historical crop data and the modified weighting factors of the array of vegetation indices.
7. The method of claim 1 , further comprising:
receiving, with the computer, zonal statistics data associated with the geographic region,
wherein said generating, with the computer, the predicted crop yield comprises generating the predicted crop yield additionally based on the zonal statistics data.
8. The method of claim 1 , wherein said generating, with the computer, the predicted crop yield is further based on one or more of the following: weather data, demographic data, and zonal statistics data.
9. The method of claim 1 , wherein the predicted crop yield is a first predicted crop yield, and further comprising:
generating, with the computer, a second predicted crop yield based on the first predicted crop yield and the modified weighting factors of the array of vegetation indices.
10. The method of claim 9 , wherein generating the second predicted crop yield is further based on one or more of the following: weather data, demographic data, and zonal statistics data.
11. A non-transitory, tangible, computer-readable media having computer-readable instructions stored thereon, for use with a computer, that when executed by the computer, cause the computer to:
receive image data of a geographic region, the image data including pixels;
generate an array of vegetation indices based on the received image data utilizing a plurality of vegetation index generating components, each operable to generate a respective individual vegetation index based on the received image data, wherein each vegetation index in the array of vegetation indices is associated with a respective weighting factor;
receive crop data comprising training data for classifying one or more of the pixels as a type of crop;
generate a masked vegetation index based on the array of vegetation indices and the received crop data, such that the pixels are associated with vegetation index data from each vegetation index in the array of vegetation indices and such that the pixels are associated with a classification as a type of crop;
receive historical crop yield data; and
generate, utilizing multivariate regression, a first predicted crop yield based on the masked vegetation index, and a second predicted crop yield based on the historical crop yield data, wherein the multivariate regression includes utilizing the weighting factors of the array of vegetation indices and includes modifying the weighting factors of the array of vegetation indices based on past accuracy of results of individual ones of the array of vegetation indices, such that accuracy of the second predicted crop yield in relation to the historical crop yield data is increased.
12. The non-transitory, tangible, computer-readable media of claim 11 , wherein the computer-readable instructions that when executed by the computer, cause the computer to generate the array of vegetation indices based on the received image data further cause the computer to generate the array of vegetation indices as an array of normalized difference vegetation indices.
13. The non-transitory, tangible, computer-readable media of claim 11 ,
wherein to generate the second predicted crop yield comprises generating the second predicted crop yield based further on one or more of the following: weather data, demographic data, zonal statistics data, and demographic data.
14. The non-transitory, tangible, computer-readable media of claim 11 , wherein the computer-readable instructions that when executed by the computer, cause the computer to generate the first predicted crop yield based on the masked vegetation index and one or more of the following: weather data, zonal statistics data, and demographic data.