Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8.

Abstract

The growth of strawberries is influenced by environmental diversity and spatial dispersion, which present significant challenges for accurate identification and real-time image processing in complex environments. This paper addresses these challenges by proposing an advanced recognition model based on YOLOv8, tailored for strawberry identification. In this study, we enhanced the YOLOv8 architecture by replacing the traditional backbone with an EfficientNetV2 feature extraction network and using ODConv instead of the standard convolution. The loss function was modified with a dynamic nonmonotonic focusing mechanism, and WiseIoU was introduced to replace the traditional CIoU. The experimental results showed that the proposed model outperformed the original YOLOv8 regarding mAP50, precision, and recall, with improvements of 16.91%, 14.92%, and 8.4%, respectively. Additionally, the model's lightness increased by 15.67%. The proposed model demonstrated superior accuracy in identifying strawberries of different ripeness levels. The improvements in the proposed model indicate its effectiveness in strawberry recognition tasks, providing more accurate results in varying environmental conditions. The lightweight nature of the model makes it suitable for deployment on picking robots, enhancing its practical applicability for real-time processing in agricultural settings.

Plant phenotyping relevance

YOLOv8画像認識モデルを改良し、イチゴ果実の成熟度という観察可能な植物器官の状態を推定する手法を開発・評価しており、単なる収穫対象の位置検出を超えている。

Code and data availability

The paper describes a custom strawberry image dataset (1,680 original images, augmented to 3,500) and an improved YOLOv8 model, but no public dataset, code, or model deposit is provided. The data availability statement only promises raw data from the authors upon request, and no public URL or repository is given.

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