Based on Internet-of-Things and multi-sensor technology, an intelligent wireless monitoring system was developed to obtain field ecological parameters and provide forest fire warning in real-time. The GPRS and China’s Beidou satellite communication were selectively used for date transmission in the field with weak cell phone signals. This monitoring system is mainly composed of several field ecological monitoring stations, a cloud server, and online system software. Atmosphere, soil, sunlight and plant parameters of different regions are obtained real-time by sensors stably and reliably. This system has functions such as field ecological data storage, dynamic query, report generation, and data analysis. As an example of typical application, the forest fire weather grade, which was supplemented with the litter layer soil humidity, was calculated to realize the early warning of the local forest fire in this system through continuous experiments at Beijing Jiufeng National Forest Park from March to May 2017 and Inner Mongolia from March to June 2018. The success ratios of data transmission through Beidou satellite were 98.57%, 99.43%, 99.59%, and 98.85%, respectively, in Beijing, and through GPRS were 99.89% and 99.90% in Inner Mongolia. Long-term real-time field ecological monitoring and forest fire warning were successfully realized. This system can be widely used for big data field acquisition and analysis in forest and agriculture regions.

This paper designs a plant leaf expansion pressure non-destructive detection device, aiming to promote plant leaf expansion pressure research and achieve precision irrigation. The design is based on leaf expansion pressure probe technology, which can effectively monitor the plant leaf expansion pressure by detecting the feedback of the leaf under constant pressure. In this paper, the stability of the sensor and the calibration model is tested. The calibration experiments showed that the coefficient of determination R² of the sensor was over 0.99, the static test results showed that the range of the sensor was 0–300 kPa, and the fluctuation of the sensor was less than 0.2 kPa during the long-term stability test. The indoor comparison tests showed that there was a significant difference in the variation of leaf expansion pressure data between plants under drought conditions and normal conditions. The irrigation experiments showed that the leaf expansion pressure was very sensitive to irrigation. The correlation between the expansion pressure data and the environmental factors was analyzed. The correlation coefficient between expansion pressure and light intensity was found to be 0.817. The results of the outdoor experiments showed that there was a significant difference in the expansion pressure of plants under different weather conditions. The data show that the plant leaf expansion pressure non-destructive detection device designed in this paper can be used both as an effective means of detecting plant leaf expansion pressure and promoting the research of plant physiological feedback mechanisms and precision irrigation.