YOLOv8-TEA: Recognition Method of Tender Shoots of Tea Based on Instance Segmentation Algorithm
With the continuous development of artificial intelligence technology, the transformation of traditional agriculture into intelligent agriculture is quickly accelerating. However, due to the diverse growth postures of tender shoots and complex growth environments in tea plants, traditional tea picki...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/6/1318 |
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| Summary: | With the continuous development of artificial intelligence technology, the transformation of traditional agriculture into intelligent agriculture is quickly accelerating. However, due to the diverse growth postures of tender shoots and complex growth environments in tea plants, traditional tea picking machines are unable to precisely select the tender shoots, and the picking of high-end and premium tea still relies on manual labor, resulting in low efficiency and high costs. To address these issues, an instance segmentation algorithm named YOLOv8-TEA is proposed. Firstly, this algorithm is based on the single-stage instance segmentation algorithm YOLOv8-seg, replacing some C2f modules in the original feature extraction network with MVB, combining the advantages of convolutional neural networks (CNN) and Transformers, and adding a C2PSA module following spatial pyramid pooling (SPPF) to integrate convolution and attention mechanisms. Secondly, a learnable dynamic upsampling method is used to replace the traditional upsampling, and the CoTAttention module is added, along with the fusion of dilated convolutions in the segmentation head to enhance the learning ability of the feature fusion network. Finally, through ablation experiments and comparative experiments, the improved algorithm significantly improves the segmentation accuracy while effectively reducing the model parameters, with mAP (Box) and mAP (Mask) reaching 86.9% and 86.8%, respectively, and GFLOPs reduced to 52.7. |
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| ISSN: | 2073-4395 |