Aug 2026· International Journal of Engineering Research and Science & Technology· 0 citations
TL;DR
A scalable image-based framework for automatic rice leaf disease detection and monitoring using deep learning and cloud-enabled analytics and enables deployment on smartphones, drones, and edge devices for smart agriculture applications.
Abstract
Rice is one of the world's most important staple crops, feeding more than half of the global population. However, rice production is significantly affected by leaf diseases such as bacterial leaf blight, blast, brown spot, and tungro, resulting in substantial yield losses. Traditional disease diagnosis relies on manual field inspection, which is time-consuming, subjective, and unsuitable for large-scale monitoring. This paper proposes a scalable image-based framework for automatic rice leaf disease detection and monitoring using deep learning and cloud-enabled analytics. The proposed framework integrates image preprocessing, data augmentation, lightweight convolutional neural networks (CNNs), transfer learning, and IoT-enabled monitoring to provide accurate disease identification in realtime. A MobileNetV3-EfficientNet hybrid architecture optimized using Bayesian hyperparameter tuning is employed to classify healthy and diseased rice leaves. Extensive experiments demonstrate that the proposed framework achieves an overall accuracy of 98.94%, precision of 98.71%, recall of 98.66%, F1-score of 98.68%, and AUC of 99.31%, outperforming existing deep learning models. The proposed scalable architecture enables deployment on smartphones, drones, and edge devices for smart agriculture applications.
The primary contribution of this work lies in the empirical demonstration that MobileNetV2, without architectural modification, can serve as a practical and accessible diagnostic tool when integrated into a web-based deployment pipeline, offering a favorable trade-off between accuracy and computational cost compared to heavier architectures.
Ammar Kamil Al Abror, Melika Debiyana Putri, Yunanda Rizki Sitompul et al.· bit-Tech· 0 citations
Performance evaluation using accuracy, model size, time per image, and number of parameters showed that the proposed model achieved high accuracy and provided better discrimination between visually similar disease classes.
RICE-MuSTA is introduced, a framework designed to jointly address multimodality, severity estimation, and uncertainty in rice leaf disease monitoring, and compressing the model into a lightweight architecture suitable for mobile and edge deployment.
Rohit P. Chavda, Kamlesh R. Makvana, V. Barot et al.· International journal of com...· 0 citations
Plant leaf diseases significantly reduce agricultural productivity and crop yield worldwide, making early and accurate detection essential to prevent large-scale crop damage. Traditional disease identification methods rely on manual inspection by experts, which is time-consuming, costly, and often inaccessible to farmers in rural areas. This paper proposes an AI-based leaf disease detection system using deep learning and transfer learning, in which EfficientNetB5 serves as a pretrained feature extractor to classify 38 plant disease categories spanning 14 crop species. Preprocessing includes HSV-based leaf segmentation, resizing to 456×456 pixels, and EfficientNet-specific normalization. A compact two-layer dense classifier is trained on the 2,048-dimensional feature vectors produced by the frozen backbone. The system achieves an overall validation accuracy of 96.49%, macro-average precision of 0.97, recall of 0.96, and F1-score of 0.96 on 2,280 held-out images. Beyond classification, the system provides actionable cure and precautionary recommendations for every detected disease, making it directly useful to smallholder farmers. Comparative analysis with ResNet50, VGG16, and MobileNetV2 confirms that EfficientNetB5 achieves the highest accuracy with a favorable parameter-to-performance ratio. Multi-class ROC evaluation further demonstrates strong discriminative capability across all disease categories.
Kuppala Ajay Kumar, Yella Sai Krishna, R. Kumar et al.· 2026 6th International Confe...· 0 citations
The FRCNN-FCMWeight model provides strong support for agricultural use cases and improves overall model generalization and enhances training robustness and identifies diseased regions effectively under highly variable field conditions.
The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Shilpa T S, K. U, A. Jajur. J· World Journal of Advanced En...· 0 citations
