Leaf disease detection using open CV

Plant diseases affect the growth and crop yield of the plants and make social, ecological and economic impacts on agriculture.Recent studies on leaf diseases show how they harm the plants.Plant leaf diseases also cause significant economic losses to farmers.Early detection of the diseases deserve special attention.Plant diseases are studied in the literature, mostly focusing on the biological aspects.They make predictions according to the visible surface of plants and leaves.Detection of diseases as soon as they appear is a vital step for effective disease management.The detection is traditionally carried out by human experts.Human experts identify diseases visually but they faces some difficulties that may harm their efforts.In this context, detecting and classifying diseases in an exact and timely manner is of the great importance.The early detection of diseases is important in agriculture for an efficient crop yield.The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes.Automatic methods for classification of plant diseases also help taking action after detecting the symptoms of leaf diseases.This paper presents a Convolutional Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm based method for tomato leaf disease detection and classification.The dataset contains 500 images of tomato leaves with four symptoms of diseases.We have modeled a CNN for automatic feature extraction and classification.Color information is actively used for plant leaf disease researches.In our model, the filters are applied to three channels based on RGB components.The LVQ has been fed with the output feature vector of convolution part for training the network.The experimental results validate that the proposed method effectively recognizes four different types of tomato leaf diseases.In this, Most content-based image retrievals (CBIR) use color as image features.However, image retrieval using color features often gives disappointing results because in many cases, images with similar colors do not have similar content.Color methods incorporating spatial information have been proposed to solve this problem .