Abstract
Disease detection in crops and plants is essential for production of good and improved quality of food, life and a stable agricultural economy. It becomes tedious and time consuming to observe the infected parts of plants manually. Recourses with proper expertise are also required to have continuous monitoring. Digital image processing along with computer vision techniques can be applied to automate early detection of plant diseases and it can save significant amount of resources. In this paper, an automated approach based on image processing and machine learning techniques is proposed which can detect three major kinds of diseases Downy Mildew, Frogeye Leaf Spot and Septoria Leaf Blight that affects apple, grapes, soybean, tomatoes and many other major plants of economic value. Generally, leaves are the most affected part of the plants. So, instead of the whole plant, concentration is given on the leaf. In this paper, image pre-processing methods like noise removal and contrast enhancement followed by colour space transformation and k-means clustering is used to segment affected parts of soybean leaves, after that both texture and colour features are extracted from segmented samples and Support Vector Machine (SVM) classification is used to separate three kinds of diseases mentioned previously.
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Datta, A., Dey, A., Dey, K.N. (2019). Automatic Multiclass Classification of Foliar Leaf Diseases Using Statistical and Color Feature Extraction and Support Vector Machine. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_1
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