Monitoring and controlling rice diseases using Image processing techniques
Development of an automated system for identifying and classifying different diseases of the contaminated plants is an emerging research area in precision agriculture. Identification of the diseases is the key to prevent qualitative and quantitative loss of agricultural yields. Rice (Oryza Sative) is one of the essential crops in India and losses due to the diseases badly impact the economy. Manual detection of the diseases is very difficult and not accurate. This creates a need for Image processing techniques which will help in accurate and timely detection of the diseases and overcome the limitations of the human vision. A new technique to diagnose and classify rice diseases has been proposed in this paper. Four diseases namely rice bacterial blight, rice blast, rice brown spot and rice sheath rot have been identified and classified. Different features like shape, the color of a diseased portion of the leaf have been extracted by developing an algorithm. All the extracted features have been combined as per the diseases and diseases have been classified using Minimum Distance Classifier (MDC) and k Nearest Neighbor classifier (k NN) The performance of the proposed technique has been evaluated with the help of 115 rice leaf images of four diseases and 70 percent image data has been used for training the classifier and 30 percent has been used for testing. Classification accuracy has been calculated for each disease using both classifiers. The overall accuracy achieved by using k NN and MDC is 87.02 percent and 89.23 percent respectively.
2016 International Conference on Computing, Analytics and Security Trends (CAST)