MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK

Electronics and Communication Engineering

The plant Disease Detection using CNN project is used to early and precise detection of plant diseases is vital for reducing crop loss and maintaining agricultural sustainability. This research suggests a deep learning-based method applying convolutional neural networks (CNNs) for automatic plant disease detection. The method adopts a systematic workflow consisting of data collection, preprocessing, model development, training, and assessment. A carefully selected dataset of images of healthy and diseased plants is used to train the CNN to identify characteristic patterns and features with respect to plant diseases. The model develops its ability to classify with an improvement that comes from recognizing even minute visual indications of various diseases. The new method enhances the effectiveness of early disease detection, which could reduce losses to agriculture and improve productivity. In addition, incorporating deep learning in precision agriculture highlights the role of technology-based solutions in alleviating critical agricultural challenges. Automating disease detection allows intervention strategies to be put in place promptly, enabling more efficient crop management practices and enhancing worldwide food security. The scalability and flexibility of deep learning algorithms also offer the potential for ongoing refinement and improvement of disease detection systems. In general, the use of CNNs in plant disease detection is a major breakthrough in agricultural technology that provides a robust, scalable, and efficient solution with far-reaching implications for sustainable agriculture and food production.

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