Optimising maize threshing process with temporal proximity soft actor-critic deep reinforcement learning algorithm

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING Biosystems Engineering Pub Date : 2024-11-05 DOI:10.1016/j.biosystemseng.2024.11.001
Qiang Zhang , Xuwen Fang , Xiaodi Gao , Jinsong Zhang , Xuelin Zhao , Lulu Yu , Chunsheng Yu , Deyi Zhou , Haigen Zhou , Li Zhang , Xinling Wu
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Abstract

Maize threshing is a crucial process in grain production, and optimising it is essential for reducing post-harvest losses. This study proposes a model-based temporal proximity soft actor-critic (TP-SAC) algorithm to optimise the maize threshing process in the threshing drum. The proposed approach employs an LSTM model as a real-time predictor of threshing quality, achieving an R2 of 97.17% and 98.43% for damage and unthreshed rates on the validation set. In actual threshing experiments, the LSTM model demonstrates an average error of 5.45% and 3.83% for damage and unthreshed rates. The LSTM model is integrated with the TP-SAC algorithm, acting as the environment with which the TP-SAC interacts, enabling efficient training with limited real-world data. The TP-SAC algorithm addresses the temporal correlation in the threshing process by incorporating temporal proximity sampling into the SAC algorithm's experience replay mechanism. TP-SAC outperforms the standard SAC algorithm in the simulated environment, demonstrating better sample efficiency and faster convergence. When deployed in actual threshing operations, the TP-SAC algorithm reduces the damage rate by an average of 0.91% across different feed rates compared to constant control. The proposed TP-SAC algorithm offers a novel and practical approach to optimising the maize threshing process, enhancing threshing quality.
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利用时间临近软行为批判深度强化学习算法优化玉米脱粒过程
玉米脱粒是谷物生产中的一个关键过程,优化这一过程对于减少收获后的损失至关重要。本研究提出了一种基于模型的时间临近软行为批判(TP-SAC)算法,用于优化脱粒滚筒中的玉米脱粒过程。所提出的方法采用 LSTM 模型作为脱粒质量的实时预测器,在验证集上,损坏率和未脱粒率的 R2 分别达到 97.17% 和 98.43%。在实际脱粒实验中,LSTM 模型对损坏率和未脱粒率的平均误差分别为 5.45% 和 3.83%。LSTM 模型与 TP-SAC 算法相结合,充当了 TP-SAC 的交互环境,从而可以利用有限的真实世界数据进行高效训练。TP-SAC 算法在 SAC 算法的经验重放机制中加入了时间邻近性采样,从而解决了脱粒过程中的时间相关性问题。在模拟环境中,TP-SAC 的性能优于标准 SAC 算法,表现出更高的采样效率和更快的收敛速度。在实际脱粒操作中,与恒定控制相比,TP-SAC 算法在不同喂入速率下平均降低了 0.91% 的损坏率。所提出的 TP-SAC 算法为优化玉米脱粒过程、提高脱粒质量提供了一种新颖实用的方法。
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来源期刊
Biosystems Engineering
Biosystems Engineering 农林科学-农业工程
CiteScore
10.60
自引率
7.80%
发文量
239
审稿时长
53 days
期刊介绍: Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.
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