Exploration of the Development Path of Rural Smart Agriculture Driven by Artificial Intelligence
Abstract
Against the backdrop of global climate change and increasing constraints on agricultural resources, developing smart agricultural technologies is a key path to achieving food security and sustainable development. This study aims to investigate the development path of rural smart agriculture driven by artificial intelligence, with a focus on addressing the bottlenecks in traditional agricultural prediction models in terms of accuracy, efficiency, and resource consumption. This study focuses on winter wheat and summer corn rotation farmland, and collects environmental data through a Pico W+ESP32-CAM sensor network. After wavelet denoising, outlier detection, and Kalman smoothing interpolation preprocessing, the soil moisture prediction effects of various models are compared. Based on the predicted fruit, rule-based irrigation control is implemented. The core indicators include crop yield, protein and vitamin content, water and fertilizer usage, and model performance (response time, error, trainable parameters, etc.). Quantum Neural Network-Back Propagation model is constructed by combining an IoT multi-sensor data acquisition system. This study innovatively integrates quantum bits and parameterized quantum gates into the prediction module. The results showed that in terms of prediction, the accuracy of the soil moisture prediction model reached 97.48%, which was 10.29% higher than that of the traditional Back Propagation Neural Network (87.19%). The model response time was only 1.27 seconds, and the computational resource utilization rate has been reduced to 23.18%, meeting the real-time decision-making needs of farmland. The irrigation strategy based on the predicted results increased water resource utilization efficiency by 47.57%, reduced fertilizer usage by 19.41%, and increased economic benefits per unit area by 28.30%. The research method has achieved high-precision dynamic prediction of soil moisture. This research verified the feasibility of artificial intelligence algorithms in agricultural edge computing scenarios, provided a closed-loop solution of "perception decision execution" for rural smart agriculture, and promoted the transformation of agricultural production to precision and low-carbon.References
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