Herbaceous Plant Leaf Disease Identification

The precise identification and control of foliar diseases in herbaceous species play a crucial part in maintaining agricultural efficiency and environmental equilibrium. This research tackles the difficulties in pinpointing foliar diseases by merging sophisticated image analysis methods with artific...

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Bibliographic Details
Published in:	2024 IEEE 9th International Conference for Convergence in Technology (I2CT) pp. 1 - 6
Main Authors:	Prasad, Arpana, Asha, V, Sreeja, S P, P, Dheeraj, Ajay, Hussain, G. Saber
Format:	Conference Proceeding
Language:	English
Published:	IEEE 05-04-2024
Subjects:	Convolutional Neural Network Diseased leaves Efficient Net Feature extraction Herbaceous plant Image analysis Image preprocessing Machine learning Machine Learning Classification Merging Minimization Support vector machines Surveillance
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Summary:	The precise identification and control of foliar diseases in herbaceous species play a crucial part in maintaining agricultural efficiency and environmental equilibrium. This research tackles the difficulties in pinpointing foliar diseases by merging sophisticated image analysis methods with artificial intelligence algorithms. In this study, a comprehensive collection of over 10,000 high-definition images was amassed, showcasing a wide variety of herbaceous plants afflicted by prevalent diseases such as leaf rust, blight, and powdery mildew. These images underwent a series of feature extraction processes to underscore the disease signatures. We employed and assessed several machine learning frameworks, particularly Convolutional Neural Networks and Support Vector Machines With regard to their precision and operational efficacy. The outcomes suggested that the CNN model excelled, achieving an impressive 95% accuracy rate in pinpointing diseases, thereby surpassing conventional diagnostic approaches. It not only provides a powerful solution for swift and precise disease detection in herbaceous plants but also paves the way for future advancements in automated plant health surveillance systems. The findings help farming methodologies, suggesting a pathway towards enhanced disease control and minimization of crop loss.
ISBN:	9798350394450
DOI:	10.1109/I2CT61223.2024.10543593