Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data

In the light of recent advances in spectral imaging technology, highly flexible modeling methods must be developed to estimate various soil and crop parameters for precision farming from airborne hyperspectral imagery. The potential of artificial neural networks (ANNs) for the development of in-season yield mapping and forecasting systems was examined. Hyperspectral images of corn (Zea mays L.) plots in eastern Canada, subjected to different fertilization rates and various weed management protocols, were acquired by a compact airborne spectral imager. Statistical and ANN approaches along with various vegetation indices were used to develop yield prediction models. Principal component analysis was used to reduce the number of input variables. Greater prediction accuracy (about 20% validation RMSE) was obtained with an ANN model than with either of the three conventional empirical models based on normalized difference vegetation index, simple ratio, or photochemical reflectance index. No clear difference was observed between ANNs and stepwise multiple linear regression models. Although the high potential usefulness of ANNs was confirmed, particularly in the creation of yield maps, further investigations are needed before their application at the field scale can be generalized.