Detection and Classification of Plant Diseases Using Soft Computing Techniques

Accurate and fast detection of plant disease can be a great boon to crop yields. Curbing the complete cost to affordable amount is also a serious concern. The present manual technique for the detection of disease is a time consuming process and many times farmers with humble background can not afford it. Thus, an automation is needed to make this hectic process fast and well within budget of farmers with low budget. This paper discusses the monitoring of plant disease using image processing and soft computing techniques by taking samples of tomato leaves. In the initial phase, training dataset is created from the collected and enhanced images. Then, a test dataset is prepared arbitrarily and multiclass SVM is utilized for obtaining the classification results. This paper discusses the image segmentation method and feature analysis as well.