Structural optimization and performance evaluation of a sugarcane leaf mulching machine

Existing sugarcane leaf mulching machines struggle to process high-fiber, tough sugarcane leaves, leading to incomplete mulching and uneven residue distribution. These limitations hinder subsequent farming operations and increase energy consumption. To address these challenges, this study presents a structural optimization and performance analysis of the 1GYF-150 sugarcane leaf mulching machine, introducing an enhanced, high-efficiency mulching mechanism. The operational principles of the machine were analyzed, and the effects of different blade types, including straight and hammer-shaped blades, on mulching performance were evaluated. Key parameters—such as blade structure, rotational speed, and arrangement—were optimized to improve mulching quality and pick-up efficiency. Further, a balance analysis of the cutter roller was conducted, incorporating MATLAB optimization algorithms and a fuzzy reliability function to enhance the roller’s structural integrity and reduce weight. Field tests under typical post-harvest conditions (leaf moisture content of 31.8%, representing the average humidity of sugarcane leaves in tropical regions) demonstrated that the optimized machine achieved a pick-up rate of 98.4% and a mulching rate of 94.4% (≤20 cm), reflecting improvements of 0.8% and 7.1% over the previous design, respectively. This study provides a valuable reference for advancing sugarcane leaf mulching machine performance and offers insights into more effective utilization of sugarcane leaf resources.

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Publication Details

Journal
Smart Agricultural Technology
Published
2025-06-17
DOI
https://doi.org/10.1016/j.atech.2025.101116
Citations
1
Primary Topic
Agricultural Engineering and Mechanization
Type
article
Field-Weighted Citation Impact
0.71
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article

Structural optimization and performance evaluation of a sugarcane leaf mulching machine

Lijiao Wei, Wei‐Hua Huang, Ming Fu Li, Shuo Wang et al.
1 citations
Smart Agricultural Technology
Agricultural Engineering and Mechanization
0.71
article

Structural optimization and performance evaluation of a sugarcane leaf mulching machine

Lijiao Wei, Wei‐Hua Huang, Ming Fu Li, Shuo Wang, Zhenhui Zheng, Dongjie Du, Chang Ge, Zhaojun Niu
article en
1 citations

Abstract

Existing sugarcane leaf mulching machines struggle to process high-fiber, tough sugarcane leaves, leading to incomplete mulching and uneven residue distribution. These limitations hinder subsequent farming operations and increase energy consumption. To address these challenges, this study presents a structural optimization and performance analysis of the 1GYF-150 sugarcane leaf mulching machine, introducing an enhanced, high-efficiency mulching mechanism. The operational principles of the machine were analyzed, and the effects of different blade types, including straight and hammer-shaped blades, on mulching performance were evaluated. Key parameters—such as blade structure, rotational speed, and arrangement—were optimized to improve mulching quality and pick-up efficiency. Further, a balance analysis of the cutter roller was conducted, incorporating MATLAB optimization algorithms and a fuzzy reliability function to enhance the roller’s structural integrity and reduce weight. Field tests under typical post-harvest conditions (leaf moisture content of 31.8%, representing the average humidity of sugarcane leaves in tropical regions) demonstrated that the optimized machine achieved a pick-up rate of 98.4% and a mulching rate of 94.4% (≤20 cm), reflecting improvements of 0.8% and 7.1% over the previous design, respectively. This study provides a valuable reference for advancing sugarcane leaf mulching machine performance and offers insights into more effective utilization of sugarcane leaf resources.

Smart Agricultural TechnologyVol. 12
Chinese Academy of Tropical Agricultural Sciences (CN), Ministry of Agriculture (EE), Precision for Medicine (United States) (US), Ministry of Agriculture (CA), Ministry of Agriculture and Rural Affairs (CN)
Zero hunger
Openalex Percentile: Top 31%
Agricultural Engineering and Mechanization
0.71
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