Advancements in Deep Learning for Plant Disease Prediction: A CNN-Based Approach for Precision Agriculture
1Dhammabhushan R raibole 2Prof T.N Ghorsad
1Student , Department of CSE 2Assistant Professor , Department of CSE
1Takshashila Institute of Engineering & Technology, Darapur
2Takshashila Institute of Engineering & Technology, Darapur
1dhammabhushan.r@gmail.com 2raj.ghorsad@gmail.com
Abstract—
Plant diseases significantly impact agricultural productivity, leading to economic losses and food security concerns. Traditional methods of disease identification rely on human expertise, which can be time-consuming and error-prone. Deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a powerful tool for automated plant disease detection. This paper explores the latest advancements in CNN-based plant disease prediction, discussing model architectures, datasets, training techniques, and real-world applications in precision agriculture. By leveraging deep learning, farmers can detect plant diseases early, enabling timely intervention and improved crop yield.
Index Terms—Deep Learning, Convolutional Neural Networks (CNN), Plant Disease Prediction, Precision Agriculture, Image Classification, AI in Agriculture
DOI link – https://doi.org/10.69758/GIMRJ/2504I5VXIIIP0061
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