PRECISION FARMING: DEEP LEARNING TECHNIQUES FOR CROP DISEASE DETECTION

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Published Jul 18, 2025
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Vol. 10 No. 2 (2025): Volume 10 Issue 2

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vaibhavi L.Vispute
Department of Electronics and Telecommunication Engineering, NES’S Gangamai College of Engineering, NMU University, Nagaon, Dhule–424 005, Maharashtra, India.
Jagruti S.Patil
Department of Electronics and Telecommunication Engineering, NES’S Gangamai College of Engineering, NMU University, Nagaon, Dhule–424 005, Maharashtra, India.
Chandrashekhar V. Patil
Department of Electronics and Telecommunication Engineering, NES’S Ganagamai College Of Engineering, Nagaon

Abstract

Crop disease is a serious problem for agricultural performance, food security and economic stability around the world. Early and accurate detection of agricultural diseases is important to minimize losses, maintain culture health and ensure optimal profitability. This study examines the use of Adhesive Neural Networks (CNNs) for the automatic detection and classification of agricultural diseases using images. Known for exceptional indicators in image recognition problems, CNNs are used to analyze visual models and symptoms of disease in cultured leaves. This study uses modern CNN architectures such as VGG16, ResNet, Inception, and Densenet to develop reliable detection structures. The study highlights the potential to transform deep agricultural education by reducing reliance on manual testing in high-intensity labor forces and promoting sustainable agricultural processes. The results of this study contribute to improving agriculture accuracy and paving the way for in-depth research in the integration of artificial intelligence-based solutions in agricultural systems.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite
[1]
vaibhavi L.Vispute, Jagruti S.Patil, and Chandrashekhar V. Patil, “PRECISION FARMING: DEEP LEARNING TECHNIQUES FOR CROP DISEASE DETECTION”, IEJRD - International Multidisciplinary Journal, vol. 10, no. 2, p. 11, Jul. 2025.

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