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Plant Disease Diagnosis System for Improved Crop Yield

Identification of the symptoms of plant diseases by means of image processing techniques is of prime concern in the area of research. There is a need for a plant disease diagnosis system that may support farmers during their daily struggle against disease outbreaks. The proposed work is a development of an efficient diagnosis system that focuses on plant disease identification by processing acquired digital images of leaves of the plant. These images are made to undergo a set of preprocessing methods for image enhancement. The enhanced image is segmented using k-means and thresholding based segmentation approaches to extract the region of interest i.e., diseased portion. Later, a satisfying set of visual features from the region of interest are extracted by applying first-level Haar Wavelet Transform method for detecting diseases accurately. Based on the extracted features set, a Fuzzy Logic based classifier identifies the type of the disease being occurred. Finally, treatment measures are provided to control the disease. The advisory helps farming community in effective decision making to protect their crop from diseases and increase its productivity. Thereby, the proposed approach improves crop yield and uplifts the economy of farming community.