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Plant Disease Classification Using Deep Learning for Agricultural Applications
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Author(s): K. R. Jansi (SRM Institute of Science and Technology, India), A. L. Amutha (SRM Institute of Science and Technology, India), A. Bhavani Shankar (SRM Institute of Science and Technology, India), J. P. Adesh (SRM Institute of Science and Technology, India)and Krishna Kant (SRM Institute of Science and Technology, India)
Copyright: 2025
Pages: 26
Source title:
Harnessing AI in Geospatial Technology for Environmental Monitoring and Management
Source Author(s)/Editor(s): Froilan D. Mobo (Philippine Merchant Marine Academy, Philippines)
DOI: 10.4018/979-8-3693-8104-5.ch010
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Abstract
Agriculture is of paramount importance to human existence, but, the presence of plant diseases poses a significant risk to both food safety and productivity. Although traditional techniques can be used to identify plant diseases, they are frequently time-consuming and ineffective. Thorough research can increase the early detection of problems and boost agriculture by providing accurate diagnosis and preventive solutions. This study supports worldwide efforts to achieve sustainable food production. This study use pre-trained Convolutional Neural Networks (CNN) models to classify numerous plant illnesses concurrently, surpassing the existing constraint of predicting just one disease at a time. The task involves fine-tuning the CNN and DenseNet169 models by training them on the Plant Village dataset. The DenseNet169 architecture was utilized in the proposed approach to obtain an accuracy of 99.61% in identifying and classifying diseases in pepper bell, potato, and tomato. This demonstrates the efficacy of deep learning in the classification of plant diseases.
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