Development of Data Mining Techniques for Leaf Disease Detection
Farmers in rural India have minimal access to agricultural experts, who can inspect crop images and render advice. Delayed expert responses to queries often reach farmers too late. We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases by using data mining techniques. The proposed solution is an improvement to the solution proposed in  as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in . The proposed system is a software solution for automatic detection and computation of texture statistics for plant leaf diseases. The developed processing scheme consists of four main steps, first a color transformation structure for the input RGB image is created, and then, we apply device-independent color space transformation for the color transformation structure. Next, in the second phase, the images at hand are segmented using the K-means clustering technique. In the third phase, we calculate the texture features for the segmented infected objects. Finally, in the fourth phase the extracted features are passed through a pre-trained neural network. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases.