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Thermal Sensing for Automated Irrigation Management of Maize and Soybean in Nebraska

Sandeep Bhatti, University of Nebraska - Lincoln


Irrigation decision making can be complex and time consuming with big data generated through remote and ground-based sensing. Automation for irrigation decision making will be beneficial to producers which will save them time and money. This study evaluated the use of pivot-mounted sensors and a decision support system called irrigation-scheduling-supervisory-control-and-data-acquisition system (ISSCADA) for automated management of irrigation in sub-humid climate of the Great Plains. The ISSCADA system was compared to the conventional method and spatial evapotranspiration model (SETMI) on basis of seasonal irrigation applied, crop yield, and water productivity. The irrigation scheduling methods were applied at four irrigation levels: 0%, 50%, 100%, and 150% of the full irrigation prescribed. The 58-ha maize-soybean field was located at the University of Nebraska’s research station near Mead, NE. The center pivot irrigation system was used as a moving platform for thermal infrared thermometer (IRT) and multispectral (MS) sensors. The MS and IRT sensors on the center pivot were compared to various sensors mounted using ground-based or aerial platforms. Canopy temperature measurements from IRTs were utilized to estimate the integrated crop water stress index (iCWSI). The iCWSI relation to weather, crop yield, and evapotranspiration (ET) was also studied to evaluate if the IRTs could be used to detect crop water stress at an early stage before the onset of yield limiting stress. The iCWSI thresholds for maize and soybean were also developed in this study. Results indicated that the pivot-mounted thermal sensors had high correlations and low mean errors when compared to the stationary sensors. The average crop water stress was found to increase with decrease in seasonal ET in all cases. However, the yield loss with reduction in ET was observed only in rainfed crop demonstrating canopy temperature data was successfully implemented to detect stress before yield loss occurred in irrigated treatments. ISSCADA and SETMI reduced irrigation applications over the common method while optimizing crop yield. Future studies should focus on reducing cost and viability of these automated systems.

Subject Area

Agricultural engineering

Recommended Citation

Bhatti, Sandeep, "Thermal Sensing for Automated Irrigation Management of Maize and Soybean in Nebraska" (2022). ETD collection for University of Nebraska-Lincoln. AAI29165830.