- Crop Production: irrigation, water management, machine learning
The interaction between human-engineered systems (e.g., irrigation) and natural processes needs to be modelled explicitly with an approach that can intelligently quantify the influence of incomplete/ambiguous information on decision-making processes. New developments in machine learning (ML) and artificial intelligence (AI) techniques hold great potential to unlock the value of big data for precision irrigation technology development. By directing our research on Increasing Sustainability of Existing Farming Practices, Appropriate Technology and Natural Resources/ Conservation/ Water Quality, we seek to develop an intelligent irrigation decision support system (DSS) that can seamlessly learn and optimize crop water use system. This research will leverage our existing data to pilot the development of a novel ML technique to support the next generation of irrigation DSS. Our proposed approach will estimate the daily irrigation needs of a 7-acre crop field irrigated by a center pivot system. This new system will enable a closed-loop control scheme to adapt the irrigation scheduling to local perturbations such as soil moisture variations, rainfall, temperature, and crop evapotranspiration. We propose to use Deep Reinforcement Learning (DRL) as a type of ML technique that enables an intelligent agent to learn an optimal policy to conserve irrigation water in an interactive environment by trial and error using feedback from its past actions and experiences. Our approaches will be validated on a field irrigated by a center pivot located at Clemson University's Edisto Research and Education Centre (REC), near Blackville, South Carolina.
Project objectives from proposal:
Focusing on the interface of three SARE focus areas, i.e., Increasing Sustainability of Existing Farming Practices, Appropriate Technology, and Natural Resources/ Conservation/ Water Quality, the PRECISION project will address two main objectives: (i) Develop an intelligent irrigation DSS that will have the potential to benefit precision irrigation and rule-based irrigation scheduling, and (ii) Provide irrigation decisions that can add value to farm profitability and irrigation management.
The DRL-based irrigation scheduling software will be directly applicable in farms where identical climatic, soil conditions, and crop evapotranspiration are available. However, before its application in irrigated areas with different climate and soil characteristics, the tool's effectiveness will have to be optimized by evaluating its performance with respect to irrigation water use efficiency and yield production. To do so, a 7-acre field irrigated by a center pivot located at the Clemson Edisto REC, near Blackville, SC will be used as the demonstration site for this effort. We will focus on one crop (i.e., cotton) water use simulation. Daily climate data consisting of rainfall, average temperature, average relative humidity and solar radiation, volumetric water content data are monitored daily at 4 locations in the site since 2012. In addition, the depth and timing of applied irrigation water data are also available since 2012 that will be used to calibrate and validate/optimize the DRL models. The proposed DSS will be field tested using Clemson ongoing project at the Edisto REC by automating irrigation based on soil moisture data (monitored by a wireless sensor network). In the ongoing experiment at the Edisto REC, three irrigation treatments are being evaluated in which irrigation is automatically applied to cotton when the weighted-average soil moisture reaches either 30, 40, or 50 kPa using four replications. Soil moisture is measured using Watermark moisture sensors installed at three depths in each plot. These results and data will be used to validate the DSS model. The graduate student will spend fall 2022 and spring 2023 at the Edisto REC to work closely with an irrigation specialist (Dr. Jose Payero), to meet with the farmers and collect the data.
The developed software will be introduced and freely shared with the Cooperation Extension programs (where climatic and soil moisture data are available) across southeast land-grant universities to test and evaluate its effectiveness and performance in other demonstration sites. The software will benefit both small and large-scale farmers by helping them to increase their seasonal net return through adequate use of irrigation water in a way that does not affect the yield.