• Mr. Sameer Rayeen
• Mr Rajneesh Pachouri
• Mr. Anurag Jain

rapid identification of plant diseases is crucial for effective agricultural management and minimizing crop losses. This thesis presents an automated system for plant disease classification using the AlexNet Convolutional Neural Network (CNN), implemented in MATLAB. With a comprehensive dataset of ten distinct plant disease classes, the model achieved an impressive accuracy of 99.61%. The proposed system encompasses several key stages, including image acquisition, preprocessing through contrast enhancement and resizing, feature extraction using AlexNet, and classification into specific disease categories. To optimize performance, hyperparameters such as learning rate, epochs, batch size, and optimizer were meticulously fine-tuned. The model’s effectiveness is validated using metrics such as precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). This research demonstrates the transformative potential of deep learning in agriculture, offering a scalable, efficient, and cost-effective tool for early disease detection and improved crop management, ultimately contributing to food security.

Plant Disease Identification, Deep Learning, AlexNet CNN, MATLAB, Automated Agriculture, Image Classification.

"Deep Learning-Based Plant Disease Detection Using AlexNet CNN for Automated Classification and Early Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.k241-k248, May-2025, Available :http://www.jetir.org/papers/JETIR2505B17.pdf

"Deep Learning-Based Plant Disease Detection Using AlexNet CNN for Automated Classification and Early Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppk241-k248, May-2025, Available at : http://www.jetir.org/papers/JETIR2505B17.pdf