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@article{179880,
        author = {Vaishnav Bhor and Dishali Bhoir and Anushka Kale and Aakanksha Bhusewar and Om Bhavsar and Prakash Sharma and Dr. Jyoti Kanjalakar},
        title = {Automated Plant and Leaf Disease Detection Using Convolutional Neural Networks for Sustainable Agriculture},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8913-8917},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179880},
        abstract = {Plant 
diseases 
significantly 
impact 
agricultural productivity, leading to economic losses and 
food insecurity. Early and accurate detection of plant 
and leaf diseases is critical to mitigating these effects. 
This research explores the application of deep learning 
techniques, specifically Convolutional Neural Networks 
(CNNs), for automated disease detection. We analyze 
several publicly available datasets containing images of 
healthy and diseased leaves, employing advanced 
preprocessing techniques, such as image normalization, 
augmentation, and resizing, to enhance model 
performance. Additionally, we perform hyperparameter 
tuning to optimize the CNN model for better 
generalization and accuracy. This study aims to 
demonstrate the potential of deep learning in automating 
plant disease detection and offers a scalable solution for 
sustainable agriculture, contributing to more effective 
and timely management of plant health.},
        keywords = {Disease Detection, Convolutional Neural  Networks,  Deep Learning, Image Processing,  Sustainable Agriculture Technology.},
        month = {May},
        }