{"title":"Optimising maize threshing process with temporal proximity soft actor-critic deep reinforcement learning algorithm","authors":"Qiang Zhang , Xuwen Fang , Xiaodi Gao , Jinsong Zhang , Xuelin Zhao , Lulu Yu , Chunsheng Yu , Deyi Zhou , Haigen Zhou , Li Zhang , Xinling Wu","doi":"10.1016/j.biosystemseng.2024.11.001","DOIUrl":null,"url":null,"abstract":"<div><div>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 R<sup>2</sup> 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.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 229-239"},"PeriodicalIF":4.4000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1537511024002368","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
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.
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.