Abstract
In agriculture, disease detection is important for a productive crop yield. So many diseases will affect crop quality of tomatoes, potatoes and pepper; some of them are bacterial spot, septoria leaf spot and yellow curved leaf diseases. To classify diseases of plant by detecting symptoms of leaf disease through automatic methods, this paper presents a Mask R-convolution neural network (Mask R-CNN) algorithm for tomato leaf, pepper leaf and potato leaf. In this, we are using plant village dataset which contains more than 1000 images of potato, tomato and pepper leaves of each plant along with disease symptoms. With the help of Mask R-CNN, classification and extraction are done automatically. Mostly information of color of the leaf is used in disease detection. Based on RGB components, filters are used in our model for the three channels. The results of proposed method for the experiment will be recognized efficiently for different types of potato, tomato and pepper leaves. This technique of detecting plant leaf disease detection using Mask R-CNN will help small holder farmers for detecting diseases of plants in very efficient manner.
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Ranjana, P., Reddy, J.P.K., Manoj, J.B., Sathvika, K. (2022). Plant Leaf Disease Detection Using Mask R-CNN. In: Hu, YC., Tiwari, S., Trivedi, M.C., Mishra, K.K. (eds) Ambient Communications and Computer Systems. Lecture Notes in Networks and Systems, vol 356. Springer, Singapore. https://doi.org/10.1007/978-981-16-7952-0_28
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DOI: https://doi.org/10.1007/978-981-16-7952-0_28
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