H2K – A robust and optimum approach for detection and classification of groundnut leaf diseases.

• The proposed method H2K is a fusion of Harris corner detector, HOG (Histogram on Oriented Gradient) and KNN classifier for accurate detection and classification of groundnut leaf diseases. • The H2K method works well on sample images and detects and classifies 5 major groundnut leaf diseases. • It includes late spot detection which is difficult to control if it is not identified in the early stages. • The H2K is robust and optimum in classifying groundnut leaf diseases with improved accuracy of 97.67%. One of the greatest key factors contributing to low yield and destruction of groundnut plant growth is leaf disease attack. The groundnut plants are prone to the diseases such as fungi, soil borne and viruses. The leaf disease detection and identification in the early stage is needed to control the spread of infection and also helps to get the maximum yield. The detection and classification of groundnut leaf diseases through naked eye observation by an expert is expensive that too developing countries. So, providing a software based solution for the above task is of great significance. In this paper, an image processing based approach that automatically identifies and categorizes the groundnut leaf diseases has been presented. The proposed method H2K is a fusion of Harris corner detector, HOG (Histogram on Oriented Gradient) and KNN classifier for accurate detection and classification of groundnut leaf diseases. It comprises of number of steps viz. image acquisition, image pre-processing by applying binary mask, HSV segmentation to segment the disease affected part, features detection and extraction using H2K (Harris, HOG and K-Nearest Neighbor) based classification of groundnut leaf diseases. The existing works concentrates on leaf diseases that are commonly occurring in any crops, but this paper proposed a robust and optimum approach for detection and classification of all major leaf diseases for Groundnut crop which is first of its kind. Thus the H2K method aids to improve the crop production and maximizes the yield. The proposed method H2K works well on sample images and detects and classifies 5 major groundnut leaf diseases including late spot which is difficult to control if it is not identified in the early stages. The existing Multiclass SVM is taken for comparison and the examined results shows that the H2K is robust and optimum in classifying groundnut leaf diseases with improved accuracy of 97.67%. [ABSTRACT FROM AUTHOR]