A digital twin-enabled fog-edge-assisted IoAT framework for Oryza Sativa disease identification and classification

Abstract

The integration of agri-technology with the Internet of Agricultural Things (IoAT) is revolutionizing the field of smart agriculture, particularly in diagnosing and treating Oryza sativa (rice) diseases. Given that rice serves as a staple food for over half of the global population, ensuring its healthy cultivation is crucial, particularly with the growing global population. Accurate and timely identification of rice diseases, such as Brown Leaf Spot (BS), Bacterial Leaf Blight (BLB), and Leaf Blast (LB), is therefore essential to maintaining and enhancing rice production. In response to this critical need, the research introduces a timely detection system that leverages the power of Digital Twin (DT)-enabled Fog computing, integrated with Edge and Cloud Computing (CC), and supported by sensors and advanced technologies. At the heart of this system lies a sophisticated deep-learning model built on the robust AlexNet neural network architecture. This model is further refined by including Quaternion convolution layers, which enhance colour information processing, and Atrous convolution layers, which improve depth perception, particularly in extracting disease patterns. To boost the model's predictive accuracy, the Chaotic Honey Badger Algorithm (CHBA) is employed to optimize the CNN hyperparameters, resulting in an impressive average accuracy of 93.5 %. This performance significantly surpasses that of other models, including AlexNet, AlexNet-Atrous, QAlexNet, and QAlexNet-Atrous, which achieved respective accuracies of 75 %, 84 %, 89 %, and 91 %. Moreover, the CHBA optimization algorithm outperforms other techniques like CSO, BSO, PSO, and CJAYA and demonstrates optimal results with an 80–20 % training-testing parameter split. Service latency analysis further reveals that the Fog-Edge-assisted environment is more efficient than the Cloud-assisted model for latency reduction. Additionally, the DT-enabled QAlexNet-Atrous-CHBA model proves to be far superior to its non-DT counterpart, showing substantial improvements in 18.7 % in Accuracy, 17 % in recall, 19 % in Fβ-measure, 17.3 % in specificity, and 13.4 % in precision, respectively. These enhancements are supported by convergence analysis and the Quade rank test, establishing the model's effectiveness and potential to significantly improve rice disease diagnosis and management. This advancement promises to contribute significantly to the sustainability and productivity of global rice cultivation.