Research and Experiments on Adaptive Root Cutting Using a Garlic Harvester Based on a Convolutional Neural Network

Ke Yang, Yunlong Zhou, Hengliang Shi, Rui Yao, Zhaoyang Yu, Yanhua Zhang, Baoliang Peng, Jiali Fan and Zhichao Hu ()
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Ke Yang: School of Automobile and Rail Transit, Luoyang Polytechnic, Luoyang 471900, China
Yunlong Zhou: School of Automobile and Rail Transit, Luoyang Polytechnic, Luoyang 471900, China
Hengliang Shi: School of Automobile and Rail Transit, Luoyang Polytechnic, Luoyang 471900, China
Rui Yao: School of Automobile and Rail Transit, Luoyang Polytechnic, Luoyang 471900, China
Zhaoyang Yu: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Yanhua Zhang: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Baoliang Peng: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Jiali Fan: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Zhichao Hu: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China

Agriculture, 2024, vol. 14, issue 12, 1-25

Abstract: Aimed at the problems of a high leakage rate, a high cutting injury rate, and uneven root cutting in the existing combined garlic harvesting and root-cutting technology, we researched the key technologies used in a garlic harvester for adaptive root cutting based on machine vision. Firstly, research was carried out on the conveyor alignment and assembly of the garlic harvester to realize the adjustment of the garlic plant position and the alignment of the bulb’s upper surface before the roots were cut, to establish the parameter equations and to modify the structure of the conveyor to form the adaptive garlic root-cutting system. Then, a root-cutting test using the double-knife disk-type cutting device was carried out to examine the root-cutting ability of the cutting device. Finally, a bulb detector trained with the IRM-YOLO model was deployed on the Jetson Nano device (NVIDIA, Jetson Nano(4GB), Santa Clara, CA, USA) to conduct a harvester field trial study. The pass rate for the root cutting was 82.8%, and the cutting injury rate was 2.7%, which tested the root cutting performance of the adaptive root cutting system and its field environment adaptability, providing a reference for research into combined garlic harvesting technology.

Keywords: garlic; adaptive root cutting; cutting height control; harvester; object detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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