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@article{176500,
        author = {Habban A.Q Bhoira and Waheeda Dhokley and Abdul Mannan M. Shaikh and Shaheed S Pathan},
        title = {Comparative Study of YOLOv8 and Faster R-CNN for Crop Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5511-5518},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176500},
        abstract = {The productivity of crops, important in global terms, is significantly affected by diseases on crops and is one of the biggest hindrances to global food security. Early intervention in detecting and diagnosing plant diseases is warranted to prevent further loss of crop yields and sustain agricultural practices. This study evaluates and compares the performance of two cutting-edge deep learning models, YOLOv8 and Faster R-CNN, regarding the detection of crop diseases, with a specific focus on cotton plants. All diseased and healthy cotton plant images formed a comprehensive dataset that was used for training and evaluation purposes. The models were evaluated through precision, recall, F1-score, inference time, and accuracy metrics, with further experiments toward analysing their real-time and high-accuracy suitabilities. This comparison discusses the trade-offs between speed and accuracy in these models and provides very important insight into the practical applicability of these models in precision agriculture. These foundation studies pave the way for utilizing advanced machine learning tools for monitoring and managing crop health.},
        keywords = {YOLO-v8, Faster-R CNN, Disease. F1 Score, Accuracy, Metrics, Cotton.},
        month = {April},
        }