REVOLUTIONIZING TEA CULTIVATION: A NOVEL DCNN APPROACH FOR HIGH- PRECISION LEAF DISEASE CLASSIFICATION
Keywords:
Deep Convolutional Neural Networks (DCNN), Tea Leaf Disease Classification, Agricultural Artificial Intelligence, Precision Agriculture, Image-Based Plant Disease DetectionAbstract
In an era where precision agriculture is becoming a cornerstone for sustainability, the early and accurate detection of plant diseases is crucial. This paper introduces a cutting-edge Deep Convolutional Neural Network (DCNN) designed to tackle the nuanced task of tea leaf disease classification. Our DCNN model transcends traditional diagnostic approaches by achieving a remarkable 99.4% accuracy rate, a testament to its advanced feature extraction capabilities and robust classification performance. The model does a great job of dealing with the high variation and similarity between classes of disease symptoms by using depthwise separable convolutions and global average pooling. Extensive validation against a comprehensive dataset demonstrates the model's efficacy and establishes its potential for real- world application. The model's precision significantly contributes to agricultural AI, promising to enhance yield, reduce loss, and pave the way for intelligent crop management systems.
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