Abstract
Agriculture is the main key component of the Indian economy. As a result, the role that food crops play crucial for both the environment and people. Many plants die as a result of poor disease diagnosis and lack of awareness of the disease’s symptoms, which directly affects the economy. India, being an agriculture-based country, farmers belong to the type of people, who are not much sound in both technical and English literacy, need scientific methods and proper knowledge on plant disease. This paper discusses the detection of plant disease from a plant leaf image using CNN algorithm and provides corresponding biological and chemical control measures in the user preferred language (English, Kannada, and Hindi) and those categories of farmers, who are unable to read/write even their own language, can gain access to detected disease and controls in speech format.
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Lavanya, A., Ganavi, N., Sowmya, M.R. (2024). CNN Approach for Plant Disease Detection—Krishi Snehi. In: Shetty, N.R., Prasad, N.H., Nagaraj, H.C. (eds) Advances in Communication and Applications . ERCICA 2023. Lecture Notes in Electrical Engineering, vol 1105. Springer, Singapore. https://doi.org/10.1007/978-981-99-7633-1_26
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DOI: https://doi.org/10.1007/978-981-99-7633-1_26
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