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 device comprising:
an image data receiving component operable to receive image data of a geographic region;
a vegetation index generation component operable to generate an array of vegetation indices based on the received image data, said vegetation index generation component including a plurality of vegetation index generating components, each operable to generate a respective individual vegetation index based on the received image data;
a crop data receiving component operable to receive crop data comprising training data for classifying an image pixel as a type of crop;
a masking component operable to generate a masked vegetation index based on the array of vegetation indices and the received crop data; and
a multivariate regression component operable to generate a predicted crop yield based on the masked vegetation index.
2. The device of claim 1 , wherein said vegetation index generation component is operable to generate the array of vegetation indices including a normalized difference vegetation index.
3. The device of claim 1 , wherein said image data receiving component is operable to receive the image data of a geographic region as RGB and infrared image data of the geographic region.
4. The device of claim 1 , further comprising:
a crop yield data receiving component operable to receive historical crop yield data,
wherein said multivariate regression component is operable to generate the predicted crop yield additionally based on the historical crop yield data.
5. The device of claim 4 , further comprising:
a demographic data receiving component operable to receive demographic data,
wherein said multivariate regression component is operable to generate the predicted crop yield additionally based on the demographic data.
6. The device of claim 4 , further comprising:
a weather data receiving component operable to receive weather data,
wherein said multivariate regression component is operable to generate the predicted crop yield additionally based on the weather data.
7. The device of claim 1 ,
wherein the multivariate regression component is operable to generate a second predicted crop yield based on the masked vegetation index and historical crop yield data.
8. The device of claim 1 , further comprising:
a zonal statistics data receiving component operable to receive zonal statistics data associated with the geographic region,
wherein said multivariate regression component is operable to generate the predicted crop yield additionally based on the zonal statistics data.
9. A method comprising:
receiving, via an image data receiving component, image data of a geographic region;
generating, via a vegetation index generation component, an array of vegetation indices based on the received image data, the vegetation index generation component including a plurality of vegetation index generating components, each operable to generate a respective individual vegetation index based on the received image data;
receiving, via a crop data receiving component, crop data comprising training data for classifying an image pixel as a type of crop;
generating, via a masking component, a masked vegetation index based on the array of vegetation indices and the received crop data; and
generating, via a multivariate regression component, a predicted crop yield based on the masked vegetation index.
10. The method of claim 9 , wherein said generating, via the vegetation index generation component, 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.
11. The method of claim 9 , wherein said receiving, via the image data receiving component, image data of the geographic region comprises receiving multiband image data of a geographic region as an RGB and near infrared image data of the geographic region.
12. The method of claim 9 , further comprising:
receiving, via a crop yield data receiving component, historical crop yield data,
wherein said generating, via the multivariate regression component, the predicted crop yield based on the masked vegetation index comprises generating the predicted crop yield additionally based on the historical crop yield data.
13. The method of claim 12 , further comprising:
receiving, via a demographic data receiving component, demographic data,
wherein said generating, via the multivariate regression component, the predicted crop yield based on the masked vegetation index comprises generating the predicted crop yield additionally based on the demographic data.
14. The method of claim 12 , further comprising:
receiving, via a weather data receiving component, weather data,
wherein said generating, via the multivariate regression component, the predicted crop yield based on the masked vegetation index comprises generating the predicted crop yield additionally based on the weather data.
15. The method of claim 9 , wherein the predicted crop yield is a first predicted crop yield, and further comprising generating, via the multivariate regression component, a second predicted crop yield based on historical crop data.
16. The method of claim 9 , further comprising:
receiving, via a zonal statistics data receiving component, zonal statistics data associated with the geographic region,
wherein said generating, via the multivariate regression component, the predicted crop yield comprises generating the predicted crop yield additionally based on the zonal statistics data.
17. 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, via an image data receiving component, image data of a geographic region;
generate, via a vegetation index generation component, an array of vegetation indices based on the received image data, the vegetation index generation component including a plurality of vegetation index generating components, each operable to generate a respective individual vegetation index based on the received image data;
receive, via a crop data receiving component, crop data comprising training data for classifying an image pixel as a type of crop;
generate, via a masking component, a masked vegetation index based on the array of vegetation indices and the received crop data; and
generate, via a multivariate regression component, a first predicted crop yield based on the masked vegetation index, and a second predicted crop yield based on historical crop data.
18. The non-transitory, tangible, computer-readable media of claim 17 , wherein the computer-readable instructions that when executed by the computer, cause the computer to generate, via a vegetation index generation component, an 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.
19. The non-transitory, tangible, computer-readable media of claim 17 , wherein the computer-readable instructions that when executed by the computer, cause the computer to:
receive, via a crop yield data receiving component, historical crop yield data, and
wherein to generate, via the multivariate regression component, the second predicted crop yield comprises generating the second predicted crop yield based on the historical crop yield data and one or more of the following: weather data, demographic data, zonal statistics data, and demographic data.
20. The non-transitory, tangible, computer-readable media of claim 17 , wherein the computer-readable instructions that when executed by the computer, cause the computer to generate, via the multivariate regression component, the first predicted crop yield based on the masked vegetation index and one or more of the following: weather data, demographic data, zonal statistics data, and demographic data.