Detection of Famous Tea Buds Based on Improved YOLOv7 Network

Wang, Yongwei; Xiao, Maohua; Wang, Shu; Jiang, Qing; Wang, Xiaochan; Zhang, Yongnian

doi:10.3390/agriculture13061190

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Currently, there is limited research on tea bud grading detection. Wang et al. (2023) only considered using the YOLOv7 detection algorithm for tender bud detection, with a small image field of view.…”

Section: Discussionmentioning

confidence: 99%

“…Currently, there is limited research on tea bud grading detection. Wang et al (2023) only considered using the YOLOv7 detection algorithm for tender bud detection, with a small image field of view. Chen & Chen (2020) utilized the Faster RCNN algorithm to extract tea buds and picking points, but their recognition performance is not as good as the method proposed in this paper.…”

Section: Discussionmentioning

confidence: 99%

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TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection

Wang,

Wu,

Zheng

2023

Front. Plant Sci.

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IntroductionAccurate grading identification of tea buds is a prerequisite for automated tea-picking based on machine vision system. However, current target detection algorithms face challenges in detecting tea bud grades in complex backgrounds. In this paper, an improved YOLOv7 tea bud grading detection algorithm TBC-YOLOv7 is proposed.MethodsThe TBC-YOLOv7 algorithm incorporates the transformer architecture design in the natural language processing field, integrating the transformer module based on the contextual information in the feature map into the YOLOv7 algorithm, thereby facilitating self-attention learning and enhancing the connection of global feature information. To fuse feature information at different scales, the TBC-YOLOv7 algorithm employs a bidirectional feature pyramid network. In addition, coordinate attention is embedded into the critical positions of the network to suppress useless background details while paying more attention to the prominent features of tea buds. The SIOU loss function is applied as the bounding box loss function to improve the convergence speed of the network.ResultThe results of the experiments indicate that the TBC-YOLOv7 is effective in all grades of samples in the test set. Specifically, the model achieves a precision of 88.2% and 86.9%, with corresponding recall of 81% and 75.9%. The mean average precision of the model reaches 87.5%, 3.4% higher than the original YOLOv7, with average precision values of up to 90% for one bud with one leaf. Furthermore, the F1 score reaches 0.83. The model’s performance outperforms the YOLOv7 model in terms of the number of parameters. Finally, the results of the model detection exhibit a high degree of correlation with the actual manual annotation results ( R2 =0.89), with the root mean square error of 1.54.DiscussionThe TBC-YOLOv7 model proposed in this paper exhibits superior performance in vision recognition, indicating that the improved YOLOv7 model fused with transformer-style module can achieve higher grading accuracy on densely growing tea buds, thereby enables the grade detection of tea buds in practical scenarios, providing solution and technical support for automated collection of tea buds and the judging of grades.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection

Wang,

Wu,

Zheng

2023

Front. Plant Sci.

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“…Two papers describe highly adaptable chassis and mobile platforms ("feet"), which focuses on machine-soil relationship [16,17]. Two papers described precise identification and positioning systems ("eyes"); these studies applied the improved YOLO algorithm to the recognition of tea and apples [18,19]. Two papers described sensitive decision-making and control systems ("brain") [20,21]; these studies use intelligent control algorithms, such as BP neural network algorithms, to control actuating components or for fault diagnosis.…”

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confidence: 99%

“Eyes”, “Brain”, “Feet” and “Hands” of Efficient Harvesting Machinery

Shen,

Tang,

Xiao

2023

Agriculture

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A marigold corolla detection model based on the improved YOLOv7 lightweight

Fan,

Tohti,

Geni

et al. 2024

SIViP

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Marigold, as an important traditional Chinese medicine with the functions of clearing heat and detoxification, protecting the liver, beautifying the face as well as promoting the health of the eyes, etc. Its demand is increasing day by day, and mechanized harvesting has become an inevitable trend in the industrialization of marigold harvesting. Therefore, this study tries to collect a new dataset in the south of Xinjiang, China, which is important for the production of marigold focusing on improving the YOLOv7 model by lightweighting, and proposing a set of detection models suitable for marigold. By deleting the redundant object detection layer of the YOLOv7 model, replacing the ordinary convolution of the backbone network with the DSConv convolution, replacing the SPPCSPC module with Simplified SPPF, and finally pruning and fine-tuning the model, it seeks to solve the trouble of the difficulty in deploying the mobile devices of the marigold picking robot and the inability to realize the high real-time detection. The experimental result shows that the accuracy and average accuracy mAP0.5 of the improved YOLOv7 model in marigold detection reach 93.9% and 97.7%, which are both improved compared with the original YOLOv7 model; the GFLOPs is only 2.3, which is 2.2% of the original model; and the parameter amount is 15.04M, which is 41.2% of the original model; The FPS is 166.7, which is 26.7% higher than the original model. It shows excellent accuracy and speed in detecting marigolds, providing strong technical support for marigold harvesting.

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Detection of Famous Tea Buds Based on Improved YOLOv7 Network

Cited by 5 publications

References 18 publications

TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection

TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection

“Eyes”, “Brain”, “Feet” and “Hands” of Efficient Harvesting Machinery

A marigold corolla detection model based on the improved YOLOv7 lightweight

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