Abstract:

[Objective] Within the field of plant phenotyping feature extraction, the accurate delineation of small targets boundaries and the adequate recovery of spatial details during upsampling operations have long been recognized as significant obstacles hindering progress. To address these limitations, an improved U-Net architecture designed for greenhouse sweet cherry image segmentation. [Methods] Taking temporal phenotypic images of sweet cherries as the research subject, the U-Net segmentation model was employed to delineate the specific organ regions of the plant. This architecture was referred to as the U-Net integrating self-supervised contrastive learning method for plant time-series images with priori distance embedding (PDE) pre-training and graph convolutional networks (GCN ) skip connection for greenhouse sweet cherry image segmentation. To accelerate model convergence, the pre-trained weights derived from the PDE plant temporal image contrastive learning method were transferred to. Concurrently, the incorporation of a GCN local feature fusion layer was incorporated as a skip connection to optimize feature fusion, thereby providing robust technical support for image segmentation task. The PDE plant temporal image contrastive learning method pre-training required the construction of image pairs corresponding to different phenological periods. A classification distance loss function, which incorporated prior knowledge, was employed to construct an Encoder with adjusted parameters. Pre-trained weights obtained from the PDE plant temporal image contrastive learning method were effectively transferred and and applied to the semantic segmentation task, enabling the network to accurately learn semantic information and detailed textures of various sweet cherry organs. The Encoder module performs multi-scale feature extraction by convolutional and pooling layers. This process enabled the hierarchical processing of the semantic information embedded in the input image to construct representations that progress transitions from low-level texture features to high-level semantic features. This allows consistent extraction of semantic features from images across various scales and abstraction of underlying information, enhancing feature discriminability and optimizing modeling of complex targets. The Decoder module was employed to conduct up sampling operations, which facilitated the integration of features from diverse scales and the restoration of the original image resolution. This enabled the results to effectively reconstruct spatial details and significantly improve the efficiency of model optimization. At the interface between the Encoder and Decoder modules, a GCN layer designed for local feature fusion was strategically integrated as a skip connection, enabling the network to better capture and learn the local features in multi-scale images. [Results and Discussions] Utilizing a set of evaluation metrics including accuracy, precision, recall, and F₁-Score, an in-depth and rigorous assessment of the model's performance capabilities was conducted. The research findings revealed that the improved U-Net model achieved superior performance in semantic segmentation of sweet cherry images, with an accuracy of up to 0.955 0. Ablation experiments results further revealed that the proposed method attained a precision of 0.932 8, a recall of 0.927 4, and an F₁-Score of 0.912 8. The accuracy of improved U-Net is higher by 0.069 9, 0.028 8, and 0.042 compared to the original U-Net, U-Net with PDE plant temporal image contrastive learning method, and U-Net with GCN skip connections, respectively. Meanwhile the F₁-Score is 0.078 3, 0.033 8, and 0.043 8 higher respectively. In comparative experiments against DeepLabV3, Swin Transformer and Segment Anything Model segmentation methods, the proposed model surpassed the above models by 0.022 2, 0.027 6 and 0.042 2 in accuracy; 0.063 7, 0.147 1 and 0.107 7 in precision; 0.035 2, 0.065 4 and 0.050 8 in recall; and 0.076 8, 0.127 5 and 0.103 4 in F₁-Score. [Conclusions] The incorporation of the PDE plant temporal image contrastive learning method and the GCN techniques was utilized to develop an advanced U-Net architecture that is specifically designed and optimized for the analysis of sweat cherry plant phenotyping. The results demonstrate that the proposed method is capable of effectively addressing the issues of boundary blurring and detail loss associated with small targets in complex orchard scenarios. It enables the precise segmentation of the primary organs and background regions in sweet cherry images, thereby enhancing the segmentation accuracy of the original model. This improvement provides a solid foundation for subsequent crop modeling research and holds significant practical importance for the advancement of agricultural intelligence.

Key words: priori distance embedding, transfer learning, GCN, U-Net, skip connection, plant phenotype