Leaf Disease Detection Using AI

India is an agricultural country and most of the people are farmers. Farmers are cultivating different types of crops. These crops affected by fungi, bacteria, viruses and many more. Farmers cannot be determining accurate percentage of observed disease. Patterns of diseases are so many complexes that finding the affected areas is difficult. Therefore system that provides information about disease will play important role in disease management for farmers. For this project, we have selected clustered apple crops. There are many disease found on clustered apple-like anthracnose, leaf spot, black canker, mealy bug and many more. In this project we are going to process our input image by using computer software, the image will be collected from the farmer. Disease detection involves the steps like taking picture of affected area for image acquisition, image pre-processing, image segmentation, feature extraction and classification. In this project we are going to detect various diseases from the different part of crop by using k-means clustering algorithm and artificial neural network based on the training of images in serial database. In this database various images of different part of clustered apple are affected by disease are stored. The images are threshold to particular values after that detected image threshold are masked over the original image.The image is clustered based on the features using HOG feature extraction and SVM classifier. This method provides greater accuracy when compared to state of art methods.