Abstract
The present study is based on the image processing techniques to identify and classify fungal rust disease of Pea. Rust disease is caused by Uromyces fabae (Pers.) de Bary in the form of rust-colored pustules on the leaves. The plant disease detection is limited by human visual capabilities due to microscopic symptoms of the disease and for that image processing techniques seems to be well adapted. The goal of this paper is to detect, to identify the early symptoms of rust disease at the microscopic level. The performance of various preprocessing, feature extraction and classification techniques was evaluated on microscopic images. Finally support vector machine classifier was used to detect the leaf disease of Pea Plant. The proposed system can successfully detect and examined disease with accuracy of 89.60%. Focus has been done on the early detection of rust disease at microscopic level which avoids spreading of disease not only on the whole plant but also to the other plants.
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Singh, K., Kumar, S. & Kaur, P. Support vector machine classifier based detection of fungal rust disease in Pea Plant (Pisam sativam). Int. j. inf. tecnol. 11, 485–492 (2019). https://doi.org/10.1007/s41870-018-0134-z
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DOI: https://doi.org/10.1007/s41870-018-0134-z