Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery
IEEE Transactions on Image Processing
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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.