Abstract: In the agricultural field, paddy cultivation plays an important role. But their growths are affected by various diseases. There will be a decrease in production if the diseases aren't identified at an early stage. The main goal of this work is to develop an image processing system that will identify and classify the varied paddy plant diseases affecting the cultivation of paddy namely brown spot disease, leaf blast disease, false smut, leaf streak and bacterial blight disease. This work is split into two parts namely, paddy disease detection and recognition of paddy plant diseases. In disease detection, the affected portion of the paddy plant is identified using Haar-like features and AdaBoost classifier. In disease recognition, the paddy disease type is recognized using Scale Invariant Feature Transform (SIFT) feature and classifiers namely k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). By this approach, one can detect the disease at an early stage and can take necessary steps in time to minimize the loss of production.

Keywords: Field images, Haar-like features, AdaBoost classifier, Scale Invariant Feature Transform(SIFT), K-Nearest Neighbor(K-NN).

| DOI: 10.17148/IJIREEICE.2021.9616