An Enhanced YOLOv5 Model for Greenhouse Cucumber Fruit Recognition Based on Color Space Features

Ning Wang, Tingting Qian (), Juan Yang, Linyi Li, Yingyu Zhang, Xiuguo Zheng, Yeying Xu, Hanqing Zhao and Jingyin Zhao ()
Additional contact information
Ning Wang: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Tingting Qian: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Juan Yang: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Linyi Li: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Yingyu Zhang: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Xiuguo Zheng: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Yeying Xu: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Hanqing Zhao: Shanghai Engineering Research Center of Information Technology in Agriculture, Shanghai 201403, China
Jingyin Zhao: Shanghai Engineering Research Center of Information Technology in Agriculture, Shanghai 201403, China

Agriculture, 2022, vol. 12, issue 10, 1-15

Abstract: The identification of cucumber fruit is an essential procedure in automated harvesting in greenhouses. In order to enhance the identification ability of object detection models for cucumber fruit harvesting, an extended RGB image dataset ( n = 801) with 3943 positive and negative labels was constructed. Firstly, twelve channels in four color spaces ( RGB , YCbCr , HIS , La*b* ) were compared through the ReliefF method to choose the channel with the highest weight. Secondly, the RGB image dataset was converted to the pseudo-color dataset of the chosen channel ( Cr channel) to pre-train the YOLOv5s model before formal training using the RGB image dataset. Based on this method, the YOLOv5s model was enhanced by the Cr channel. The experimental results show that the cucumber fruit recognition precision of the enhanced YOLOv5s model was increased from 83.7% to 85.19%. Compared with the original YOLOv5s model, the average values of AP , F1 , recall rate, and mAP were increased by 8.03%, 7%, 8.7%, and 8%, respectively. In order to verify the applicability of the pre-training method, ablation experiments were conducted on SSD, Faster R-CNN, and four YOLOv5 versions (s, l, m, x), resulting in the accuracy increasing by 1.51%, 3.09%, 1.49%, 0.63%, 3.15%, and 2.43%, respectively. The results of this study indicate that the Cr channel pre-training method is promising in enhancing cucumber fruit detection in a near-color background.

Keywords: deep learning; color space; ReliefF characteristic analysis; near color recognition (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 (2)

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