Automatic Robust Segmentation Scheme for Pathological Problems in Mango Crop

Machine vision and soft computing techniques have been promising in the field of agriculture and horticulture to remove the barriers of conventional methods for detecting the plant diseases using different plant parts. Image segmentation technique is first and primary step in all the related researches such as fruit grading, leaf lesion region detection etc. In this paper, a robust technique for Mango crop using different plant parts such as Fruit, Flower and Leaf has been proposed in order to detect the disease more accurately. The captured real time images are pre-processed for illumination normalization and color space conversion before segmentation. The standard K-Means clustering scheme has been made adaptive and edge detection transforms have been applied to improve the segmentation results. Here, the objective function of K-Means clustering technique has been modified and cluster centers also have been updated to segment the diseased parts from images. The results obtained are better in the terms of both general human observation and in computational time.