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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

In this paper, a garlic combine harvester machine was designed and some influential parameters of the machine were optimized. The working parts of the machine mainly consisted of a reel, a reciprocating cutter, a seedling conveyor, a profiling depth-stop device, a digging shovel and a lifting chain. Each part had unique structural parameters and motion parameters, as different parameters would deeply affect the performance of the machine. A logistical regression algorithm was utilized to analyze the working speed of the reel, the digging depth of the reciprocating cutter and the lifting speed of the lifting chain. This paper also discussed the influence of these three functions on the damage rate based on the collected data when harvesting garlic. Specifically, each function was tested 60 times for collecting data. The experimental results showed that the order of influence of the three functions on the damage rate was the digging depth, working speed and lifting speed. Moreover, the lowest damage rate was 0.18% when the digging depth was 100 mm, the working speed was 1.05 km·h−1 and the lifting speed was 0.69 m·s−1. A validation test was taken out based on the three functions of the analysis results, and the damage rate was 0.83%, which was close to the analysis results, and proved that the analysis results were accurate and meaningful. The research results are beneficial to the development and application of the garlic combine harvester.

Detail

Judul
Towards Optimizing Garlic Combine Harvester Design with Logistic Regression
Pengarang
Zhu, Zhengbo 1   Logo VIAFID ORCID  ; Li, Wei 2   Logo VIAFID ORCID  ; Wen, Fujun 3 ; Chen, Liangzhe 2 ; Xu, Yan 4 

 Jiangsu Engineering Center for Modern Agricultural Machinery and Agronomy Technology, School of Mechanical Engineering, Yangzhou University, Yangzhou 225012, China; zhuzb@yzu.edu.cn 
 Jiangsu Key Laboratory of Media Design and Software Technology, Science Center for Future Foods, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; lzchen@stu.jiangnan.edu 
 Intelligent Manufacturing College, Guangzhou Panyu Polytechnic, Guangzhou 510620, China; wenfujun1@163.com 
 Department of Automotive Engineering, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, China 
Halaman pertama
6015
Tahun publikasi
2022
Tanggal publikasi
2022
Penerbit
MDPI AG
e-ISSN
20763417
Jenis sumber
Jurnal Akademik
Bahasa publikasi
English
ID dokumen ProQuest
2679673481
Hak cipta
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.