• Priya Jadhav
• Dr. Ritu Tandon

Smart farming environments rely on advanced sensing technologies and deep learning techniques to enable real-time crop monitoring and early detection of plant diseases. Plant diseases significantly affect agricultural productivity, making timely and accurate identification essential for effective crop management. In recent years, deep learning models have shown remarkable performance in automated plant disease prediction using image-based analysis. This study presents a deep learning-based framework for plant disease prediction in smart farming environments, integrating image preprocessing and convolutional neural network models to accurately classify healthy and diseased plant leaves. The proposed system utilizes transfer learning to improve classification performance while maintaining computational efficiency. Experimental results demonstrate high prediction accuracy and robust performance under varying conditions, supporting early disease identification and timely intervention. The framework is suitable for integration with smart farming systems, enabling continuous monitoring and data-driven decision-making. By providing an accurate and efficient plant disease prediction solution, the proposed approach contributes to sustainable agricultural management and improved crop productivity in smart farming environments.

Deep Learning, Smart Farming, Machine Learning, Image Processing, Plant Disease

"Deep Learning-Based Plant Disease Prediction in Smart Farming Environments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.b461-b469, January-2026, Available :http://www.jetir.org/papers/JETIR2601167.pdf

"Deep Learning-Based Plant Disease Prediction in Smart Farming Environments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppb461-b469, January-2026, Available at : http://www.jetir.org/papers/JETIR2601167.pdf