Study of the potential of alternative crops by integration of multisource data using a neuro‐fuzzy technique

Abstract

This work proposes a neuro‐fuzzy method for suggesting alternative crop production over a region using integrated data obtained from land‐survey maps as well as satellite imagery. The methodology proposed here uses an artificial neural network (multilayer perceptron, MLP) to predict alternative crop production. For each pixel, the MLP takes vector input comprising elevation, rainfall and goodness values of different existing crops. The first two components of the aforementioned input, that is, elevation and rainfall, are determined from contour information of land‐survey maps. The other components, such as goodness values of different existing crops, are based on the productivity estimates of soil determined by fuzzyfication and expert opinion (on soil) along with production quality by the Normalized Difference Vegetation Index (NDVI) obtained from satellite imagery. The methodology attempts to ensure that the suggested crop will also be a high productivity crop for that region.

Acknowledgment

This work was supported by the Indian Space Research Organization (ISRO) under the grant for the development of land‐cover classification methodology with fusion of data from different sensors, ref. 10/4/416, February 27, 2003. The authors would like to thank the referees for their useful comments and suggestions.