Maturity and disease detection in tomato using computer vision

Our country has vast potential to come up as a cardinal exporter of agricultural produce, but lack of quick quality evaluation techniques, huge losses in processing and handling after harvesting, diseased crop etc. result in a lower contribution to global market. The tomato crop is often infected by a disease, where plant's leaves get covered with spots of colors dark brown with purple border and light grey center; termed as Septoria Leaf Spot. It causes the leaves to turn yellow, but most damage occurs due to loss of leaves by infection. In this paper, tomato maturity based on color and fungal infection in the tomato leaves is determined. Initially thresholding algorithm was performed to determine the maturity of tomato. To make the system more generalized and self-adapting a shift to k-means clustering algorithm is made. Finally a comparative analysis of both the methods was done to analyze which method is more suitable in different conditions. Also an unconventional machine vision system has been suggested that scrutinizes the leaves emerging out of the soil and depending upon leaf spots, it analyzes the nature of fungus and its depth into the stem of tomato. k-means algorithm along with thresholding is used for segmentation of image and eventually identifying fungus. The fungus part that is segmented, is then studied to derive the percentage of presence.