YOLOv7-DWS: tea bud recognition and detection network in multi-density environment via improved YOLOv7.

Abstract

Introduction Accurate detection and recognition of tea bud images can drive advances in intelligent harvesting machinery for tea gardens and technology for tea bud pests and diseases. In order to realize the recognition and grading of tea buds in a complex multi-density tea garden environment. Methods This paper proposes an improved YOLOv7 object detection algorithm, called YOLOv7-DWS, which focuses on improving the accuracy of tea recognition. First, we make a series of improvements to the YOLOv7 algorithm, including decouple head to replace the head of YOLOv7, to enhance the feature extraction ability of the model and optimize the class decision logic. The problem of simultaneous detection and classification of one-bud-one-leaf and one-bud-two-leaves of tea was solved. Secondly, a new loss function WiseIoU is proposed for the loss function in YOLOv7, which improves the accuracy of the model. Finally, we evaluate different attention mechanisms to enhance the model's focus on key features. Results and discussion The experimental results show that the improved YOLOv7 algorithm has significantly improved over the original algorithm in all evaluation indexes, especially in the R Tea (+6.2%) and mAP@0.5 (+7.7%). From the results, the algorithm in this paper helps to provide a new perspective and possibility for the field of tea image recognition.

Plant phenotyping relevance

茶芽画像から芽の種類・葉数を認識および等級化する改良物体検出法を開発し、性能評価しており、植物器官の状態推定が中心である。

Code and data availability

The article describes a custom multi-density tea bud dataset (945 images, Labelme annotations, PASCAL VOC format) and the YOLOv7-DWS model, but no block contains any public deposit, availability statement, or author-provided URL for the dataset, images, code, or trained model. No qualifying paper-specific public assets

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