Abstract: Plants play a crucial role in food production, but diseases threaten crop yields and quality. Traditional manual inspection is time-consuming and inconsistent, while AI-powered detection offers a faster, more reliable solution. Using deep learning and CNNs, this system analyzes images of leaves, stems, and roots to classify diseases accurately. It also features an interactive chatbot to assist farmers with symptoms, treatments, and prevention. This paper explores advancements in AI-driven plant disease detection, evaluates its performance using a Kaggle dataset, and discusses challenges like dataset diversity and computing power. Future improvements aim to enhance multilingual support and accessibility for farmers. Grapes, a commercially significant crop, are highly vulnerable to leaf, stem, and fruit diseases. Early detection is essential for protecting yields. This project proposes a CNN-based deep learning approach to identify grape leaf diseases with high accuracy, reducing manual effort and providing timely decision support.
Keyword: Plant disease detection, Deep learning, CNNs, AI in agriculture, Crop health monitoring, Grape leaf disease classification, Automated disease diagnosis, Image recognition in agriculture, smart farming solutions.
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DOI:
10.17148/IJARCCE.2025.14608
