Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-11· 0 citations· 31 references
Abstract
Agriculture plays a great role in ensuring food security in the world but there are serious threats in rice production due to different types of bacterial and fungal diseases. Manual diagnosis is subjective and labor intensive and may be delayed. The proposed paper suggests a multi-class identification of ten different pathologies related to rice leaves through a multi-class Artificial Intelligence framework using the dataset of 15,023 images. Three methodological paradigms that use mobile net version two with XGBoost, a custom Convolutional Neural Network (CNN), and a novel Hybrid CNN-LSTM model that views patterns of diseases as feature sequences are benchmarked. Whereas the XGBoost classifier has the highest accuracy of 91.75 percent, the deep learning models have high feature extraction capacity. The proposed Ensemble model that incorporates both spatial and sequential learning achieves 97 percent accuracy. This paper supports the hypothesis that deep learning hybrid models have a great impact on diagnostic accuracy, which can be successfully used as an automated instrument to provide disease control in precision agriculture.
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.
Two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture are presented, showing that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model.
H. Jeiad, S. Samaan, Omar Janeh et al.· Automation· 0 citations
Agriculture is a key component in ensuring food security in the world and one of the most common staple foods in the world is rice. But there are a number of leaf diseases affecting rice, like bacterial leaf blight, brown spot, and rice blast, which cause lower production yield. In order to limit economic losses, it is...
Vandana Samanthula, N. Valli, Sultanov Sirojiddin et al.· 2026 6th International Confe...· 0 citations
Agricultural productivity and food security are heavily impacted by plant diseases, and thus there is a high demand for accurate and automated plant disease detection that can be achieved by applying deep learning techniques. This research proposes a Multi-Model Ensemble Method Based on Deep Learning for multi-plant di...
An extensive set of experiments was conducted to evaluate the performance of the proposed model for plant disease detection, and it is demonstrated that the model achieves highly reliable results, with an accuracy of 97.13%.
Hayat Meddeber, M. Meddeber· ITEGAM- Journal of Engineeri...· 0 citations
An artificial intelligence recognition model based on multi-scale convolutional neural network called LH-DenseNet is presented, which can enhance the potential of deep learning in the field of agriculture, allowing more autonomic and systematic systems emerge.
Sihai Li· International Journal of Fut...· 0 citations
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