Multi-Disease Classification and Severity Estimation of Cotton and Soybean Leaves Using Convolutional Neural Network
Keywords:
Convolutional Neural Network, Multi-Resolution Feature Optimization, Classification, Disease Detection, severity estimationAbstract
Multi-disease classification and severity estimation of cotton and soybean leaves is a crucial task in the field of agriculture. The early detection and management of plant diseases are vital for ensuring crop yield and food security. This study presents a novel approach for the multi-disease classification and severity estimation of cotton and soybean leaves using the hybridization of a Convolutional Neural Network (CNN) with Multi-Resolution Feature Optimization (MRFO). The suggested model takes the benefit of both CNN and MRFO to enhance the classification and severity estimation performance. The dataset used for training and testing consists of images of cotton and soybean leaves affected by multiple diseases. The experimental results demonstrate that the suggested model provides improved classification accuracy and severity estimation compared to the state-of-the-art approaches. The model achieved an overall accuracy of 96.07% for cotton leaves and 95.51% for soybean leaves. Moreover, the proposed model accurately estimated the severity of the diseases in the soybean and cotton leaves to be 99.1% and 91.21% respectively, which is crucial for effective disease management.
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