# Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field

arXiv: https://arxiv.org/abs/2304.01435 · HTML: https://mlsanity.com/paper/2304.01435/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 79 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors](https://mlsanity.com/paper/1903.04628/) (2019)
- [A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems](https://mlsanity.com/paper/2209.08230/) (2022)
- [Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2302.00725/) (2023)
- [Exploring Deep Reinforcement Learning for Holistic Smart Building Control](https://mlsanity.com/paper/2301.11510/) (2023)

## Built on by (most cited first, 6 of 6)

- [A Safe and Data-efficient Model-based Reinforcement Learning System for HVAC Control](https://mlsanity.com/paper/2407.12195/) (2024)
- [MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Recharge](https://mlsanity.com/paper/2407.01005/) (2024)
- [Integrating machine learning paradigms and mixed-integer model predictive control for irrigation scheduling](https://mlsanity.com/paper/2306.08715/) (2023)
- [Opportunities of Reinforcement Learning in South Africa's Just Transition](https://mlsanity.com/paper/2411.15145/) (2024)
- [Towards VM Rescheduling Optimization Through Deep Reinforcement Learning](https://mlsanity.com/paper/2505.17359/) (2025)
- [Mimir: Physics-Grounded LLM Agents for Long-Horizon Irrigation Control](https://mlsanity.com/paper/2610.02038/) (2026)

## Abstract (arXiv)

Agricultural irrigation is a significant contributor to freshwater consumption. However, the current irrigation systems used in the field are not efficient. They rely mainly on soil moisture sensors and the experience of growers, but do not account for future soil moisture loss. Predicting soil moisture loss is challenging because it is influenced by numerous factors, including soil texture, weather conditions, and plant characteristics. This paper proposes a solution to improve irrigation efficiency, which is called DRLIC. DRLIC is a sophisticated irrigation system that uses deep reinforcement learning (DRL) to optimize its performance. The system employs a neural network, known as the DRL control agent, which learns an optimal control policy that considers both the current soil moisture measurement and the future soil moisture loss. We introduce an irrigation reward function that enables our control agent to learn from previous experiences. However, there may be instances where the output of our DRL control agent is unsafe, such as irrigating too much or too little water. To avoid damaging the health of the plants, we implement a safety mechanism that employs a soil moisture predictor to estimate the performance of each action. If the predicted outcome is deemed unsafe, we perform a relatively-conservative action instead. To demonstrate the real-world application of our approach, we developed an irrigation system that comprises sprinklers, sensing and control nodes, and a wireless network. We evaluate the performance of DRLIC by deploying it in a testbed consisting of six almond trees. During a 15-day in-field experiment, we compared the water consumption of DRLIC with a widely-used irrigation scheme. Our results indicate that DRLIC outperformed the traditional irrigation method by achieving a water savings of up to 9.52%.