Abstract: The importance of plant disease detection in contemporary agriculture is emphasized in this paper, along with how it can reduce crop losses and increase food security. The emphasis is on Convolutional Neural Networks (CNNs) as a powerful instrument for automating the detection of plant diseases by examining the visual indicators present in leaf photos. The paper explores issues including dataset quality, model generalization, and real-world implementation while highlighting CNNs' outstanding ability to quickly and accurately distinguish between healthy and infected plants. Ethical issues such as responsible data use and model bias are addressed along with the need for large, diverse, and well-annotated datasets. In order to advance the creation and implementation of CNN-based plant disease detection systems, the abstract ends with a strong argument for cooperative research.
Keyword: CNN, automation, dataset, real-world, prediction, ML model.
Cite:
Namrata, Niya Rani, Shivani, Pooja Tripathi, "PLANT DISEASE DETECTION USING ML REVIEW PAPER", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13366.
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DOI:
10.17148/IJARCCE.2024.13366
