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@article{174892,
        author = {Mr.Pratik Dake and Mr.Shubham Beloshe and Mr.Sumit Gaikwad and Mrs.Swarupa Deshpande and Mr.Himanshu Sonawane and Mr.Soham Bhosale},
        title = {Enhancing Agriculture: Deep Learning Approach To Plant Monitoring And Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1329-1335},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174892},
        abstract = {Detection of plant disease plays a very important role in ensuring global food security, which has been disrupted in recent years by vigorous plant pathogens, thereby resulting in devastating crop loss and significant economic and environmental damage. Monitoring plant health at all stages of growth will avoid the outbreak of diseases on a large scale and perhaps may allow its control in advance as well. We thus propose an intelligent system with deep learning, using Convolutional Neural Networks (CNN), to monitor plant growth and detect diseases through the monitoring of three main plant components: roots, stems, and leaves. The approach overcomes traditional visual inspection’s shortcomings, offering an automated solution of high precision. The CNN model is thus trained using such diverse datasets so that it can classify different diseases and recognize symptoms in early stages to initiate effective interventions. Comprehensive analysis enhances early disease detection in plants, continuous monitoring of growth, better agricultural productivity, and food sustainability.},
        keywords = {Hyperspectral Imaging for Plant Health, Data Augmentation in Agriculture, Multi-Scale Feature Fusion, Early Disease Detection Techniques, Depth-wise Separable Convolution, Automated Plant Disease Detection, CNN-Based Disease Detection Models, Entropy-Controlled Feature Selection, Real-Time Plant Health Monitoring, Feature Fusion.},
        month = {April},
        }