论文的题目是《基于天气预报的水稻灌溉决策强化学习方法》。

A reinforcement learning approach to irrigation decision-making for rice using weather forecasts

Mengting Chen, Yufeng Luo

文章介绍了在智能灌溉决策方面的最新进展。欢迎下载引用详见：https://www.sciencedirect.com/science/article/pii/S0378377421001037

文章发表在科学导报ScienceDirect 上：https://doi.org/10.1016/j.agwat.2021.106838

文章要点

●提出并验证了灌溉决策的一种强化学习方法。

●通过明智的学习方法解决利用灌溉经验和天气预报的不确定性的问题。

●该方法能在不损失产量的前提下节约灌溉水量，缩短灌溉时间。

●所提出的灌溉强化学习方法对于智能灌溉实践具有很好的应用前景。

论文摘要

充分利用降雨提高农业用水效率是农业节水的有效途径之一。当前，天气预报可以用于潜在地节约灌溉用水，但应避免不必要灌溉的风险和由于天气预报的不确定性造成的，可能存在的产量损失。为此，提出了一种基于短期天气预报的深度Q学习灌溉决策策略。以南昌地区水稻为例，验证了该方法的实用性。收集了南昌附近台站2012-2019年水稻生育期的短期天气预报和观测气象资料。比较了常规灌溉和DQN灌溉两种灌溉决策策略，并对其节水效果进行了评价。结果表明，该模型的日降水预报性能良好，具有潜在的学习和开发空间。DQN灌溉策略训练后具有较强的泛化能力，可用于利用天气预报进行灌溉决策。在我们的案例中，模拟结果表明，与传统灌溉决策相比，DQN灌溉产生必要的节水优势，灌溉节水23mm，排水量平均减少21mm，灌溉时间平均减少1.0倍，产量没有明显下降。DQN灌溉策略借鉴了过去的灌溉经验和天气预报的不确定性，避免了天气预报不完善的风险。

Highlights

• A reinforcement learning approach for irrigation decision-making is proposed and tested.

• Past irrigation experiences and uncertainties of weather forecasts are intelligently learned.

• The proposed method can conserve irrigation water and reduce irrigation time without yield loss.

• The proposed reinforcement learning approach for irrigation is promising for smart irrigation practices.

Abstract

Improving efficiency with the use of rainfall is one of the effective ways to conserve water in agriculture. At present, weather forecasting can be used to potentially conserve irrigation water, but the risks of unnecessary irrigation and the yield loss due to the uncertainty of weather forecasts should be avoided. Thus, a deep Q-learning (DQN) irrigation decision-making strategy based on short-term weather forecasts was proposed to determine the optimal irrigation decision. The utility of the method is demonstrated for paddy rice grown in Nanchang, China. The short-term weather forecasts and observed meteorological data of the paddy rice growth period from 2012 to 2019 were collected from stations near Nanchang. Irrigation was decided for two irrigation decision-making strategies, namely, conventional irrigation (i.e., flooded irrigation commonly used by local farmers) and DQN irrigation, and their performance in water conservation was evaluated. The results showed that the daily rainfall forecasting performance was acceptable, with potential space for learning and exploitation. The DQN irrigation strategy had strong generalization ability after training and can be used to make irrigation decisions using weather forecasts. In our case, simulation results indicated that compared with conventional irrigation decisions, DQN irrigation took advantage of water conservation from unnecessary irrigation, resulting in irrigation water savings of 23 mm and reducing drainage by 21 mm and irrigation timing by 1.0 times on average, without significant yield reduction. The DQN irrigation strategy of learning from past irrigation experiences and the uncertainties in weather forecasts avoided the risks of imperfect weather forecasting.

文章来源：http://irripro.com.cn/