Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 25, Issue 9, Pages 1723-1732Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/0143116031000150068
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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.
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