Vision-Based a Seedling Selective Planting Control System for Vegetable Transplanter

Mingyong Li, Liqiang Xiao, Xiqiang Ma, Fang Yang, Xin Jin and Jiangtao Ji ()
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Mingyong Li: College of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China
Liqiang Xiao: Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471000, China
Xiqiang Ma: Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471000, China
Fang Yang: Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471000, China
Xin Jin: Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471000, China
Jiangtao Ji: Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471000, China

Agriculture, 2022, vol. 12, issue 12, 1-14

Abstract: Seedling transplanting is an important part of vegetable mechanized production in modern agriculture. After the seedlings are cultivated on a large scale by the nursery tray, they are planted into the field by the transplanter. However, unlike manual transplanting, transplanter is unable to judge the status of seedlings in the hole during seedling planting, which leads to problems such as damaged seedlings and empty holes being picked in the same order and planted into the field, resulting in yield reduction and missed planting. Aiming at this problem, we designed a seedling selective planting control system for vegetable transplanter which includes vision unit, seedling picking mechanism, seedling feeding mechanism, planting mechanism, pneumatic push rod unit, limit sensor, industrial computer and logic controller. We used asymmetrical light to construct visual identification scenes for planting conditions, which suppresses environmental disturbances. Based on the intersection operation of mask and image, a fast framework of tray hole location and seedling identification (FHLSI) was proposed combined with FCM segmentation algorithm. The vision unit provides the transplanting system with information on the status of the holes to be transplanted. Based on the information, planting system chooses the healthy seedlings for transplanting, improving the survival rate and quality of transplanting. The results show that the proposed visual method has an average accuracy of 92.35% for identification with the selective planting control system of seedlings and improves the transplanting quality by 15.4%.

Keywords: transplanter; modern agriculture; selective planting; seedling identification; vision (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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