According to Penn State researchers, an "internet of things" -or IoT- system monitoring real-time data from soil-based sensors to activate an automated precision irrigation setup can conserve water and boost crop production.
In a study involving fresh-market tomato production conducted at the Russell E. Larson Agricultural Research Center at Rock Springs, Centre County, the researchers demonstrated that a low-power, wide-area wireless network — called LoRaWAN — is a low-cost, easily implemented online arrangement effective for precision crop irrigation. The system, powered by batteries charged by solar panels, controlled the timing, rate, and distribution of water.
"These results suggest that the internet-of-things system can be implemented for precision and automatic irrigation operations for vegetable and other horticultural crops, enhancing those crops' water-use efficiency and sustainability," said team leader Long He, assistant professor of agricultural and biological engineering in the College of Agricultural Sciences. "Overall, the LoRaWAN performed well in power consumption, communication, sensor reading, and valve control."
The open field irrigation-management experiment, spearheaded by Haozhe Zhang, who recently graduated with a doctoral degree in agricultural and biological engineering, tested the effect of four irrigation-scheduling treatments on fresh-market tomato plants. The experimental treatments included irrigation based on feedback from crop evapotranspiration (the process by which water is transferred from the land to the atmosphere by evaporation from the soil and by transpiration from plants), soil moisture potential sensors, and "GesCoN," a decision-support tool for the "fertigation" of tomato. Fertigation is the injection of fertilizers into an irrigation system.
Irrigation water-use efficiency and crop yield were evaluated for each treatment. In findings recently published in Smart Agricultural Technology, the researchers reported that throughout the growing season, the overall water use efficiencies of the methods ranged from 22% to 28% above the control, and they produced 15% to 22% higher marketable fruit yield than normal.