Web-based Crop Disease Detection
Keywords:
Convolutional neural networks, Deep learning, Explainable AI, Field dataset, MobileNetV2, Plant disease detection, Remedy recommendation, Smart agriculture, Web-based deploymentAbstract
Timely detection of crop diseases is essential for minimizing yield loss and ensuring sustainable agriculture. While deep learning, particularly convolutional neural networks (CNNs), has shown promise in automated disease identification through leaf imagery, most existing systems rely heavily on lab-curated datasets or mobile-only deployments. This review presents a comprehensive survey of recent advancements in plant disease detection using deep learning, emphasizing web-based and field-deployable systems. We analyze over 34 research papers spanning dataset development, model architectures (ResNet, Mo- MobileNetV2, EfficientNet), remedy recommendation engines, and explainable AI techniques. Gaps are identified in generalizability to real-world conditions, accessibility across platforms, and integration of actionable remedies. Motivated by these insights, we outline the design of a browser-based system—CropGuard—capable of real-time image-based classification and remedy suggestion. The review highlights future research opportunities in dataset realism, cross-platform deployment, interpretability, and real-time advisory systems for smart farming.
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