Environmental risk assessment based on multiscale spatial recurrent neural network algorithm for IoT agriculture area

• Published in: Scientific reports Vol. 15; no. 1; pp. 21801 - 16
• Main Authors: Sofiya, M., Arulmozhi, M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2025; Nature Portfolio
• Subjects: 639/166/987; 639/705/117; 639/705/258; AntLion behavior optimization; Dimensionality; Exhaustive traffic information rate; Humanities and Social Sciences; Internet of things; multidisciplinary; Science; Science (multidisciplinary); Smart agriculture; Traffic; Smart agriculture; Exhaustive traffic information rate; Internet of things; MSRNNet; Dimensionality; Box-plot normalization; Traffic; AntLion behavior optimization
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-06975-x

In recent years, smart agricultural environments have gained attention for enhancing farming efficiency and productivity. These systems use smart sensors integrated with Internet of Things (IoT) devices to collect data such as temperature, soil moisture, and humidity, helping to improve yield and optimize water usage. However, high data traffic during IoT-based data collection often delays access to vital information. A key challenge is identifying and eliminating redundant traffic. Existing methods fail to analyze the marginal rate of traffic features, leading to reduced performance. This research proposes a Multiscale Spatial Recurrent Neural Network (MSRNNet) to classify IoT traffic and enhance smart agriculture systems. After data collection, Box-Plot Normalization (BPN) is applied for preprocessing. The Exhaustive Traffic Information Rate (ETIR) method evaluates the marginal rate of each feature, and the AntLion Behavior Optimization (ALBO) algorithm selects the most significant features, reducing dimensionality. The optimized dataset is then classified using the MSRNNet. Simulations using Python 3.9 and the Anaconda toolkit show the proposed model achieves 97.08% accuracy, 96.05% precision, 94.25% recall, and a 95.71% F1-score, with a low misclassification rate of 1.25% and a time complexity of 85.49 ms, demonstrating its effectiveness and reliability.