Crop Node Detection and Internode Length Estimation Using an Improved YOLOv5 Model

Jinnan Hu, Guo Li, Haolan Mo, Yibo Lv, Tingting Qian (), Ming Chen and Shenglian Lu ()
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Jinnan Hu: Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China
Guo Li: Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China
Haolan Mo: Guilin Center for Agricultural Science & Technology Research, Guilin 541004, China
Yibo Lv: Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China
Tingting Qian: Agricultural Information Institutes of Science and Technology, Shanghai Academy of Agriculture Sciences, Shanghai 201403, China
Ming Chen: Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China
Shenglian Lu: Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China

Agriculture, 2023, vol. 13, issue 2, 1-17

Abstract: The extraction and analysis of plant phenotypic characteristics are critical issues for many precision agriculture applications. An improved YOLOv5 model was proposed in this study for accurate node detection and internode length estimation of crops by using an end-to-end approach. In this improved YOLOv5, a feature extraction module was added in front of each detection head, and the bounding box loss function used in the original network of YOLOv5 was replaced by the SIoU bounding box loss function. The results of the experiments on three different crops (chili, eggplant, and tomato) showed that the improved YOLOv5 reached 90.5% AP (average precision) and the average detection time was 0.019 s per image. The average error of the internode length estimation was 41.3 pixels, and the relative error was 7.36%. Compared with the original YOLOv5, the improved YOLOv5 had an average error reduction of 5.84 pixels and a relative error reduction of 1.61%.

Keywords: plant phenotyping; node detection; internode length; YOLOv5 (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: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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