Internet of Things (IoT) has emerged prolifically in the recent years as they aid in lot of applications. In reference to the agriculture sector, automated technologies for the plant disease recognition have varying benefits, and at the same time has potential challenges. In this work, an automated plant disease detection model has been developed for the IoT environment. The proposed scheme places the nodes over the simulation environment for capturing the plant leaf images. The system maintains a sink node, which collects the information from the automated plant disease detection module and helps in IoT based monitoring. The images from the nodes are pre-processed through the median filter for making it suitable for the plant disease detection. Then, the segmentation is done over the image, and from the image, segment level and the pixel level features are extracted. This work develops a novel classifier, named sine cosine algorithm based rider neural network (SCA based RideNN) for the disease detection such that the weights in the neural network are chosen optimally. The entire detection performance is validated using the metrics, like accuracy, sensitivity, specificity, and energy of nodes on different IoT environments. The simulation results reveal that the proposed approach has improvement detection performance with the accuracy of 0.9156.

近年来，物联网（IoT）大量出现，因为它们有助于许多应用程序。在农业领域，用于植物病害识别的自动化技术具有不同的优势，同时也具有潜在的挑战。在这项工作中，针对物联网环境开发了自动植物病害检测模型。所提出的方案将节点放置在仿真环境上以捕获植物叶片图像。该系统维护一个汇节点，该汇节点从自动植物病害检测模块收集信息，并有助于基于物联网的监控。来自节点的图像通过中值滤波器进行预处理，使其适合于植物病害检测。然后，对图像进行分割，然后根据图像进行分割，提取段级和像素级特征。这项工作开发了一种新颖的分类器，称为基于正弦余弦算法的骑手神经网络（基于SCA的RideNN），用于疾病检测，从而可以最佳地选择神经网络中的权重。整个检测性能通过使用指标来验证，例如准确性，灵敏度，特异性和不同物联网环境上节点的能量。仿真结果表明，该方法具有改进的检测性能，准确度为0.9156。不同物联网环境中节点的敏感性，特异性和能量。仿真结果表明，该方法具有改进的检测性能，准确度为0.9156。不同物联网环境中节点的敏感性，特异性和能量。仿真结果表明，该方法具有改进的检测性能，准确度为0.9156。