Fused Segmentation Algorithm for the Detection of Nutrient Deficiency in Crops Using SAR Images

The main aim of this chapter is to segment nutrient deficiency in crop images using fuzzy sets (FSs) theory. Fuzziness exists in images as the quantized level of brightness in each pixels. Processing of such uncertain images can be efficiently handled by using fuzzy sets, particularly IFSs. Before initiation of segmentation, crop images taken by satellite are fused to reduce uncertainty in the captured images. Finally, the fused image is processed for segmentation of deficiency in crop images using clustering method based on interval valued intuitionistic fuzzy sets (IVIFSs) with new distance function. Quantitatively, the segmented images are evaluated using precision-recall, ROC curves, and measure for structural similarity index, and their results are compared with results of existing methods. Performance measures reveal that the proposed method seems to segment deficiency better than other comparable methods. Segmentation of nutrient deficiency using the proposed method helps the agriculturist in differentiating various types of disease thereby estimating the rate of fertilization for the crop concerned, which improves economy.