Research and Experiment on Miss-Seeding Detection of Potato Planter Based on Improved YOLOv5s

Hongling Li, Xiaolong Liu (), Hua Zhang, Hui Li, Shangyun Jia, Wei Sun, Guanping Wang, Quan Feng, Sen Yang and Wei Xing
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Hongling Li: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Xiaolong Liu: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Hua Zhang: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Hui Li: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Shangyun Jia: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Wei Sun: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Guanping Wang: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Quan Feng: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Sen Yang: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Wei Xing: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China

Agriculture, 2024, vol. 14, issue 11, 1-18

Abstract: In order to improve the performance of potato planter, reduce miss-seeding rates, enhance the overall quality of the seeding operation, and ultimately increase the yield of the potato, it is necessary to implement effective technical means to monitor and identify the miss-seeding issues during the seeding process. The existing miss-seeding detection technologies commonly use sensors to monitor, but such technologies are easily affected by factors like heavy dust and strong vibrations, resulting in poor interference resistance and adaptability. Therefore, this study aims to explore and apply deep learning algorithms to achieve real-time monitoring of the miss-seeding phenomenon in potato planter during the planting process. Considering both the lightweight of the miss-seeding detection model and its practical deployment, this study selects and adapts the YOLOv5s algorithm to achieve this goal. Firstly, the attention mechanism is integrated into the backbone network to suppress background interference and improve detection accuracy. Secondly, the non-maximum suppression algorithm is improved by replacing the original IoU-NMS with the Soft-NMS algorithm to enhance the bounding box regression rate and reduce missed detections of potato seeds due to background overlap or occlusion. Experimental results show that the accuracy of the improved algorithm in detecting miss-seeding increased from 96.02% to 98.30%, the recall rate increased from 96.31% to 99.40%, and the mean average precision ( mAP ) improved from 99.12% to 99.40%. The improved model reduces missed and false detections, provides more precise target localization, and is suitable for miss-seeding detection in natural environments for potato planter, providing technical and theoretical support for subsequent intelligent reseeding in potato planter.

Keywords: potato; spoon chain-type seed metering device; YOLOv5s; miss-seeding detection; CBAM (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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