Detection of Leaf Diseases Using Image Processing Techniques

Abstract

Agricultural field plays a vital role in the earning to feed ever growing population. The productivity of the crop depends on pest attack, disease, weather, etc. The disease in plants will affect the production of crops. Detecting diseases in plants through visualization is a traditional method which is not relevant. We are focusing on reliable, fast and accurate detection method that can be done using image processing techniques to monitor large area of agricultural land. Methods that explore visible symptoms in leaves are considered in this paper even though it can manifest in any part of the plant. Disease detection involves the steps like image acquisition, pre-processing, segmentation, feature extraction and classification. The paper contains a survey on different disease classification techniques like SVM and neural network that will be useful for plant leaf disease detection.

Keywords: SVM (support vector machine), neural network, RGB (red, green, blue), BPNN (back-propagation neural networks), image processing, SGDM (spatial gray-level dependence matrices)

Cite this Article: Lamiya J.A., Pooja R. Nair, Suraj K. Yadav, Usama Yoosuf, Sethulekshmi R., Rafeeq Ahmed K., Riyaz A. Rahiman, Vishak K.H. Detection of Leaf Diseases using Image Processing Techniques. International Journal of Image Processing and Pattern Recognition. 2019; 5(2): 14–19p.