2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 253-264, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Madhuri Kanojiya 1 , Lokesh Chouhan 2 , Vipin Tiwari 3 * , Dheresh Soni 4 , Devika A. Verma 5 , Yashwant Dongre 6
Doi: https://doi.org/10.54216/FPA.190218
One of the most important sectors for providing for daily human requirements is agriculture. At the same time, digitization has a big impact on a number of businesses, making it simpler to carry out a number of challenging tasks. In order to help the farmer and the consumer, technology and digitization must be adopted. Utilizing technology and routine monitoring, diseases can be identified and eliminated, increasing agricultural output. This paper suggests a system for recognizing and categorizing plant illnesses, initially focused on five separate classes: two fruit classes, one vegetable class, one edible pulse class, and one-grain class. The Plant Village and UCI ML Repository Dataset, which is well known as a freely accessible, accepted standard, and reliable data source, was used for this purpose. Based on them, a CNN model is prepared for analyzing them with an accuracy upto 95.42%. Image segmentation will also play a role in calculating precise amount of infection followingly, a good interface is must to utilize it in a proper way for a user which can be provided in the form of app, a feature that every user requires on daily basis.
Agriculture , Digitization , Plant Village , CNN model , Image Segmentation
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