PRECISION: leveraging deeP REinforCement learnIng algorithm for Sustainable IrrigatiON scheduling

View the project final report

Commodities

Practices

• 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 sought to advance the technology and innovative research needed to understand and develop systems that can sense, learn, and act in the agricultural environment. This research leveraged existing data to pilot the development of a novel ML technique to support the next generation of irrigation decision support systems. Our proposed approach was to estimate the daily irrigation needs of a field irrigated by a center pivot, based on soil moisture measurements and climatic variables using Deep Reinforcement Learning (DRL) as a type of ML technique that enables an intelligent agent to learn in an interactive environment by trial and error using feedback from its past actions and experiences. Fieldwork activities for this research were carried out at Clemson University's Edisto Research and Education Centre (REC) near Blackville, SC. The collected data were used to develop the proposed irrigation decision support system.

Focusing at the interface of three SARE focus areas, i.e., Increasing Sustainability of Existing Farming Practices, Appropriate Technology and Natural Resources/ Conservation/ Water Quality, PRECISION project had two main objectives: (i) Develop an intelligent irrigation decision support system that has the potential to benefit precision irrigation and rule-based irrigation scheduling, (ii) Provide irrigation decisions that can add value to farm profitability and irrigation management.

A 7-acre field irrigated by a center pivot located at Clemson’s Edisto REC, near Blackville, SC, was used as the demonstration site for this effort. The DRL-based irrigation scheduling software is directly applicable to farms whose climate and soil conditions are identical to this study area. However, before its application in irrigated areas with different climate and soil characteristics, the tool's effectiveness will have to be checked by evaluating its performance with respect to irrigation water use efficiency and yield production. For that reason, further research on how the tool can be used to optimize water use for other crops and field conditions will be required.