To address the issues of poor network coverage, low data transmission efficiency, and high power consumption in traditional soybean field monitoring, this paper proposes an intelligent monitoring solution that integrates BeiDou positioning with a low-power joint data compression algorithm. The system employs a dual-mode communication architecture combining ZigBee 3.0 and LoRa, enabling round-the-clock real-time collection and transmission of key soybean growth parameters, including air temperature and humidity, light intensity, soil temperature and humidity, soil electrical conductivity, and ph. Leveraging the BeiDou satellite navigation system, monitoring nodes can obtain precise spatial coordinates, providing a reliable geographic basis for spatial data analysis and addressing the shortcomings of traditional monitoring methods regarding insufficient spatial resolution. To overcome bandwidth limitations in long-distance wireless transmission and reduce system power consumption, this paper proposes a hybrid lossless compression algorithm based on bit-field packing, LZW coding, and Huffman coding. This algorithm offers high compression efficiency while ensuring data integrity and accuracy, significantly improving transmission efficiency and reducing the long-term energy consumption of field sensor nodes. Communication performance and power consumption test results confirm that the system delivers stable long-distance transmission and demonstrates excellent low-power performance. Error analysis of the multidimensional monitoring parameters revealed that the overall measurement error for all environmental and soil indicators was kept within 5%, meeting the requirements for high-precision monitoring throughout the entire soybean growth cycle.
Yu et al. (Tue,) studied this question.