Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree

Chun Wang, Hongxu Li, Xiujuan Deng, Ying Liu, Tianyu Wu, Weihao Liu, Rui Xiao, Zuzhen Wang and Baijuan Wang ()
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Chun Wang: College of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming 650201, China
Hongxu Li: Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China
Xiujuan Deng: Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China
Ying Liu: Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China
Tianyu Wu: Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China
Weihao Liu: Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China
Rui Xiao: Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China
Zuzhen Wang: Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China
Baijuan Wang: Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China

Agriculture, 2024, vol. 14, issue 12, 1-21

Abstract: Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens.

Keywords: improved YOLOv8; varieties of tea plants; fresh tea leaves; variety identification; dynamic upsampling; Inner-SIoU (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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