An artificial neural network model for estimating crop yields using remotely sensed information

Abstract

Crop yield forecasting is a very important task for researchers in remote sensing. Problems exist with traditional statistical modelling (especially regression models) of nonlinear functions with multiple factors in the cropland ecosystem. This paper describes the successful application of an artificial neural network in developing a model for crop yield forecasting using back-propagation algorithms. The model has been adapted and calibrated using on the ground survey and statistical data, and it has proven to be stable and highly accurate.

Acknowledgments

This work was one of the state's key projects (KZCX2-308-4) supported by Chinese Academy of Sciences. We are grateful to Yang Mugeng, of the China University of Mining and Technology, for his help on neural network programs. We also thank Mr Rosema, of the EARS Company in the Netherlands, for useful discussions about crop growth monitoring.