• Md Sajeel Alam
• Goldy Saini
• Swati Khanve
• Dr. Nitya Khare

Agriculture remains the backbone of global food security, yet plant diseases pose a persistent threat to crop yields and farmer livelihoods. Conventional disease detection methods are often costly, slow, and inaccessible to small scale farmers. To bridge this gap, this study leverages Convolutional Neural Networks (CNNs) for automated plant disease recognition using leaf image classification. CNNs, renowned for their ability to extract intricate features from visual data, offer a scalable solution for real-time disease detection. By training and optimizing CNN models on diverse datasets, this research enhances predictive accuracy while ensuring computational feasibility for deployment on resource-constrained devices. The proposed framework empowers farmers with early disease identification, enabling timely intervention and reducing economic losses. Beyond technological innovation, this work underscores the human spirit’s resilience—merging scientific advancement with the innate drive to protect and sustain the natural world. In doing so, it reinforces the harmony between technology and agriculture, advocating for sustainable farming practices that safeguard both food security and farmer well-being.

CNN, plant disease detection, agriculture, machine learning, sustainability.

"Disease Identification In Crop Plant Leaves Based On CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.k106-k110, May-2025, Available :http://www.jetir.org/papers/JETIR2505B01.pdf

"Disease Identification In Crop Plant Leaves Based On CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppk106-k110, May-2025, Available at : http://www.jetir.org/papers/JETIR2505B01.pdf