Application of Fuzzy Reasoning to Assessment of Crop Stress Level Based on Modis Data: a Focus on Heavy Metal Pollution

This paper reviews the application of fuzzy theory and its combination with artificial neural-network technology for remote sensing information extraction. A dynamic fuzzy neural-network model is presented for crop heavy metal stress level assessment based on MODIS data. Hyperspectral vegetation indices, including NDVI, EVI and NDVIg, were used as input variables in this model for the purpose of enhancing and extracting weak information of crop heavy metal stress obtained from large-scaled farmland under complex circumstances. The output error and the root mean square error were considered as system performance evaluation factors. 250 samples, which contained values of hyperspectral vegetation indices and heavy metal stress levels, were prepared for the training process. And fuzzy reasoning rules were generated and evaluated based on their significance. At the end of the training process, this dynamic fuzzy neural-network model generated a total number of seven fuzzy rules. Another dataset, with 60 testing samples, was applied to evaluate the performance of this trained system. The result of this experiment indicated that this model was capable of extracting stress information with reasonable accuracy, which is over 95%, and thus it could be used as an effective tool in monitoring and managing agricultural environment. * Corresponding author.