Optimising maize threshing by integrating DEM simulation and interpretive enhanced predictive modelling

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING Biosystems Engineering Pub Date : 2024-06-08 DOI:10.1016/j.biosystemseng.2024.06.001
Xuwen Fang, Jinsong Zhang, Xuelin Zhao, Li Zhang, Deyi Zhou, Chunsheng Yu, Wei Hu, Qiang Zhang
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Abstract

Maize threshing is a complex and dynamic process, and optimisation of operating parameters is essential to improve threshing quality and efficiency. In this study, machine learning was combined with interpretability analysis to investigate the dynamic effects of operating parameters on maize threshing quality and to optimise the threshing process. The maize cob model used to simulate threshing was validated by stacking angle and tensile test. Real-time drum operating parameters and threshing quality data obtained through Discrete Element Method (DEM) threshing simulation were used to train a threshing quality prediction network. The prediction accuracy was improved by incorporating an attention mechanism into the Long Short-Term Memory (LSTM) model with an optimised Root Mean Square Error (RMSE) of 0.0041. The global feature importance and dynamic Shapley Additive Explanations (SHAP) value analyses demonstrated that rotational speed is a key determinant of unthreshed and damaged rates and that its effect varies significantly at different stages of the threshing process. Guided by these analyses, a staged speed adjustment experiment was conducted. Specifically, an increase in rotational speed during the initial threshing phase markedly lowered the initial unthreshed rate for medium and high-speed groups to 6.63% and 2.73%, respectively, a significant improvement over the 67.70% observed in the low-speed group. The final damage rate in the high-speed group decreased by 9.79% relative to the low-speed group. This dynamic analysis approach provides a novel paradigm for optimising complex agricultural processes under varying conditions, offering interpretable insights for precise process control and improvement.

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通过整合 DEM 模拟和解释性增强预测建模优化玉米脱粒
玉米脱粒是一个复杂的动态过程,优化操作参数对提高脱粒质量和效率至关重要。本研究将机器学习与可解释性分析相结合,研究操作参数对玉米脱粒质量的动态影响,并优化脱粒过程。用于模拟脱粒的玉米棒模型通过堆叠角和拉伸试验进行了验证。通过离散元法(DEM)脱粒模拟获得的滚筒实时运行参数和脱粒质量数据被用于训练脱粒质量预测网络。通过在长短期记忆(LSTM)模型中加入注意力机制,提高了预测精度,优化后的均方根误差(RMSE)为 0.0041。全局特征重要性和动态夏普利加法解释(SHAP)值分析表明,转速是未脱粒率和受损率的关键决定因素,其影响在脱粒过程的不同阶段有显著差异。在这些分析的指导下,我们进行了分阶段转速调整实验。具体而言,在初始脱粒阶段提高转速可显著降低中速组和高速组的初始未脱粒率,分别为 6.63% 和 2.73%,与低速组的 67.70% 相比有了明显改善。高速组的最终损坏率比低速组降低了 9.79%。这种动态分析方法为在不同条件下优化复杂的农业流程提供了一种新的范例,为精确的流程控制和改进提供了可解释的见解。
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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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