Rural Ecosystem Monitoring in Food Security Analysis Based on Sustainable Agriculture: Artificial Intelligence Application

The ecological security patterns (ESPs) in the Qinling Mountains are facing significant challenges due to the worldwide issues of harsh climate and urbanization. Achieving sustainable development in China requires an understanding of the features of ESPs in the Qinling Mountains, an important ecological barrier. With a Yangxian focus, this work makes use of techniques like geographic information systems (GISs), remote sensing, and machine learning (ML). This research proposed a novel technique in rural region ecosystem monitoring-based food security based on sustainable agriculture using machine learning in artificial intelligence application. Here, input is collected as food security analysis for rural region ecosystem monitoring as well as processed for noise removal and normalization. Then, this data features are extracted and classified using reinforcement radial Gaussian encoder with adversarial Boltzmann temporal neural networks. Experimental analysis is carried out for parameters like training accuracy, random precision, sensitivity, AUC, and F-1 score. The proposed method obtained 98% of training accuracy, 96% of sensitivity, 95% of F-1 score, 97% of AUC, and 93% of random precision.