Research on IoT-Based Sweet Potato Growth Environment Monitoring and Comprehensive Evaluation System

This study addresses the limitation of single-factor environmental assessment in autonomous sweet potato farming under open-field conditions. An IoT-based sweet potato growth environment monitoring and comprehensive evaluation system was developed by integrating an STM32-based acquisition terminal, multi-sensor data collection, Narrow Band Internet of Things (NB-IoT) transmission, and cloud-based visualization. Five key environmental variables, namely soil temperature, soil moisture, soil available nitrogen, photosynthetically active radiation (PAR), and CO2, were continuously monitored. To improve the evaluation of heterogeneous and uncertain environmental information, a multi-factor environmental quality assessment method combining fuzzy membership functions and an improved D-S evidence theory was proposed. Field experiments were conducted in Danzhou, Hainan, China, and 600 valid synchronized samples were obtained for analysis. The results showed that most samples were classified as Suitable (63.5%), followed by Normal (30.8%) and Poor (5.7%), with a mean comprehensive environmental score of 0.802. Among the monitored variables, PAR and soil temperature showed relatively high adaptive weights, indicating their important roles in environmental quality discrimination. Furthermore, the comprehensive environmental evaluation result exhibited a significant positive correlation with sweet potato yield (r = 0.6501, p = 2.3724 × 10−73), demonstrating good explanatory ability for yield variation. The proposed system provides an effective technical framework for real-time environmental monitoring, quantitative suitability evaluation, and precision management in autonomous sweet potato farming.