Detection and Classification of Paddy Leaf Diseases using Artificial Neural Networkq
Paddy cultivation plays a vital role in agriculture but its growth is affected by various diseases. In todayjs digital age, it is important that the farmers get to use the latest technology for identifying the diseases. In most of the cases the farmers rely on their experience and intuition for decision on identifying crop diseases. If the symptoms are not treated properly, this leads to poor paddy production. The main objective of the work is to develop an image processing system that aims at increasing the productivity of paddy crop by early detection of three diseases namely Brown spot, Bacterial blight and Leaf smut. This work is divided in to four parts i.e. Image Acquisition of the diseased paddy leaf, Image Segmentation by k mean algorithm, Feature Extraction by Gabor filter, Finally training the Probabilistic Neural Network (PNN) and testing the classifier. This method has achieved an accuracy of 90% which proves to be accurate and the most efficient process.
Keywords - K means Clustering Algorithm, CIELab Color Space, Gabor Filter, PNN.