Recognition Method of Cabbage Heads at Harvest Stage under Complex Background Based on Improved YOLOv8n

Yongqiang Tian, Chunjiang Zhao (), Taihong Zhang, Huarui Wu and Yunjie Zhao ()
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Yongqiang Tian: School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Chunjiang Zhao: National Engineering Research Center for Information Technology in Agriculture, Beijing 100125, China
Taihong Zhang: School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Huarui Wu: National Engineering Research Center for Information Technology in Agriculture, Beijing 100125, China
Yunjie Zhao: School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2024, vol. 14, issue 7, 1-17

Abstract: To address the problems of low recognition accuracy and slow processing speed when identifying harvest-stage cabbage heads in complex environments, this study proposes a lightweight harvesting period cabbage head recognition algorithm that improves upon YOLOv8n. We propose a YOLOv8n-Cabbage model, integrating an enhanced backbone network, the DyHead (Dynamic Head) module insertion, loss function optimization, and model light-weighting. To assess the proposed method, a comparison with extant mainstream object detection models is conducted. The experimental results indicate that the improved cabbage head recognition model proposed in this study can adapt cabbage head recognition under different lighting conditions and complex backgrounds. With a compact size of 4.8 MB, this model achieves 91% precision, 87.2% recall, and a mAP@50 of 94.5%—the model volume has been reduced while the evaluation metrics have all been improved over the baseline model. The results demonstrate that this model can be applied to the real-time recognition of harvest-stage cabbage heads under complex field environments.

Keywords: cabbage; recognition and localization; object detection; deep learning; automatic harvesting (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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