Saini Ashish, Gill Nasib Singh, Gulia Preeti, Tiwari Anoop Kumar, Maratha Priti, Shah Mohd Asif
Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, 124001, India.
Department of Computer Science & Information Technology, Central University of Haryana, Mahendragarh, 123031, India.
Sci Rep. 2025 Jan 9;15(1):1456. doi: 10.1038/s41598-025-85486-1.
The Internet of Things (IoT) has recently attracted substantial interest because of its diverse applications. In the agriculture sector, automated methods for detecting plant diseases offer numerous advantages over traditional methods. In the current study, a new model is developed to categorize plant diseases within an IoT network. The IoT network is simulated for monitoring crop diseases. Routing is performed with Henry Gas Chicken Swarm Optimization (HGCSO), which is designed by integrating Henry Gas Solubility Optimization (HGSO) and Chicken Swarm Optimization (CSO). The fitness parameters of the model include delay, energy, distance, and link lifetime (LLT). At the Base Station (BS), plant disease categorization is performed by collecting plant leaf images. Preprocessing is done on the input images using median filtering. Various features, such as Histogram of Oriented Gradient (HoG), statistical features, Spider Local Image Features (SLIF), and Local Ternary Patterns (LTP) are extracted. Plant disease categorization is carried out using a Deep Residual Network (DRN), which is trained using the developed Caviar Henry Gas Chicken Swarm Optimization (CHGCSO) that combines the CAViaR model with HGCSO. Comparative results show an accuracy of 94.3%, a maximum sensitivity of 93.3%, a maximum specificity of 92%, and an F1-score of 93%, indicating that the CHGCSO-based DRN outperforms existing methods. Graphic Abstract.
物联网（IoT）因其多样的应用，近来引起了广泛关注。在农业领域，与传统方法相比，用于检测植物病害的自动化方法具有诸多优势。在当前研究中，开发了一种新模型用于在物联网网络中对植物病害进行分类。对物联网网络进行了模拟以监测作物病害。使用通过整合亨利气体溶解度优化（HGSO）和鸡群优化（CSO）设计的亨利气体鸡群优化（HGCSO）进行路由。该模型的适应度参数包括延迟、能量、距离和链路寿命（LLT）。在基站（BS），通过收集植物叶片图像进行植物病害分类。使用中值滤波对输入图像进行预处理。提取各种特征，如定向梯度直方图（HoG）、统计特征、蜘蛛局部图像特征（SLIF）和局部三元模式（LTP）。使用深度残差网络（DRN）进行植物病害分类，该网络使用将CAViaR模型与HGCSO相结合的改进的鱼子酱亨利气体鸡群优化（CHGCSO）进行训练。比较结果显示准确率为94.3%，最大灵敏度为93.3%，最大特异性为92%，F1分数为93%，表明基于CHGCSO的DRN优于现有方法。图形摘要。
