Plant Leaf Disease Detection and Classification using Image Processing

Abstract

Agriculture is the mainstay of the Indian economy. Almost 70% people depend on it & shares major part of the GDP. Diseases in crops mostly on the leaves affects on the reduction of both quality and quantity of agricultural products. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases & quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes several steps viz. image acquisition, image pre-processing, segmentation, features extraction and neural network based classification. Keywords:HSI, SGDM, GLCM, ANN, GUI, K-means clustering, BPNN, CIELAB color space I. INTRODUCTION Agriculture has played a key role in the development of human civilization. If there is decrease in agro products, total economy will get affected. Therefore judicious management of all input resources such as soil, seed, water, fertilizers etc. is essential for sustainability. As diseases are inevitable, detecting them plays major role. One can refer incident that occurred in 2007, Georgia (USA), it is estimated that approximately 539 USD was the loss incurred due to plant diseases as well as controlling them. The naked eye observation of farmers followed by chemical test is the main way of detection and classification of agricultural plant diseases. In developing countries, farming land can be much larger and farmers cannot observe each and every plant, every day. Farmers are unaware of non-native diseases. Consultation of experts for this might be time consuming & costly. Also unnecessary use of pesticides might be dangerous for natural resources such as water, soil, air, food chain etc. as well as it is expected that there need to be less contamination of food products with pesticides. There are two main characteristics of plant disease detection machine-learning methods that must be achieved, they are: speed and accuracy [1]. There is need for developing technique such as automatic plant disease detection and classification using leaf image processing techniques. This will prove useful technique for farmers and will alert them at the right time before spreading of the disease over large area. Solution is composed of four main phases; in the first phase we create a color transformation structure for the RGB leaf image and then, we apply color space transformation for the color transformation structure. Then image is segmented using the K-means clustering technique. In the second phase, unnecessary part (green area) within leaf area is removed. In third phase we calculate the texture features for the segmented infected object. Finally, in the fourth phase the extracted features are passed through a pre-trained neural network [1].