Development and Test of a Self-Propelled Peanut Combine Harvester for Hilly and Mountainous Regions

Addressing the issue of complex terrain and small field plots in hilly and mountainous regions where large combine harvesters are not suitable, this paper presented the design and development of a semi-feed self-propelled peanut combine harvester. This harvester is characterized by its small size and flexible steering. Theoretical calculations were used to determine the structural parameters of the main working components. A three-factor, three-level orthogonal experimental design was implemented, focusing on forward speed, vibration frequency and picking roller rotational speed, as these parameters significantly influence operational performance. Through this experiment, regression models were established between the total loss rate, the broken pod rate, and these three above mentioned factors. Through multi-objective optimization, it was found that when the forward speed is 0.44 m/s, the picking roller rotational speed is 350 rpm, and the vibration frequency is 6.4 Hz, the total loss rate and broken pods rate of the harvester are the lowest. Validation experiments were conducted under this parameter combination, with the total loss rate and broken pods rate being effectively reduced to 3.21% and 0.85%, respectively. The experiments proved that this harvester meets the requirements for mechanized peanut harvesting in hilly and mountainous regions.

Authors

Institutions

Publication Details

Journal
Agriculture
Published
2025-02-20
DOI
https://doi.org/10.3390/agriculture15050457
Citations
5
Primary Topic
Soil Mechanics and Vehicle Dynamics
Type
article
Field-Weighted Citation Impact
4.77
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and Test of a Self-Propelled Peanut Combine Harvester for Hilly and Mountainous Regions

Feng Wu, Zhichao Hu, Hongguang Yang, Haiyang Shen et al.
5 citations
Agriculture
Soil Mechanics and Vehicle Dynamics
4.77
article

Development and Test of a Self-Propelled Peanut Combine Harvester for Hilly and Mountainous Regions

Feng Wu, Zhichao Hu, Hongguang Yang, Haiyang Shen, Weiwen Luo, Guiying Ren, Fengwei Gu, Zhaoyang Yu, Man Gu, Liang Pan
article en
5 citations

Abstract

Addressing the issue of complex terrain and small field plots in hilly and mountainous regions where large combine harvesters are not suitable, this paper presented the design and development of a semi-feed self-propelled peanut combine harvester. This harvester is characterized by its small size and flexible steering. Theoretical calculations were used to determine the structural parameters of the main working components. A three-factor, three-level orthogonal experimental design was implemented, focusing on forward speed, vibration frequency and picking roller rotational speed, as these parameters significantly influence operational performance. Through this experiment, regression models were established between the total loss rate, the broken pod rate, and these three above mentioned factors. Through multi-objective optimization, it was found that when the forward speed is 0.44 m/s, the picking roller rotational speed is 350 rpm, and the vibration frequency is 6.4 Hz, the total loss rate and broken pods rate of the harvester are the lowest. Validation experiments were conducted under this parameter combination, with the total loss rate and broken pods rate being effectively reduced to 3.21% and 0.85%, respectively. The experiments proved that this harvester meets the requirements for mechanized peanut harvesting in hilly and mountainous regions.

AgricultureVol. 15(5)
Ministry of Agriculture and Rural Affairs (CN), Chongqing Academy of Agricultural Sciences (CN), Nanjing Institute of Agricultural Mechanization (CN), Henan Polytechnic University (CN)
Openalex Percentile: Top 6%
Soil Mechanics and Vehicle Dynamics
4.77
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.