TY - JOUR
T1 - Cherry growth modeling based on Prior Distance Embedding contrastive learning
T2 - Pre-training, anomaly detection, semantic segmentation, and temporal modeling
AU - Xu, Wei
AU - Guo, Ruiya
AU - Chen, Pengyu
AU - Li, Li
AU - Gu, Maomao
AU - Sun, Hao
AU - Hu, Lingyan
AU - Wang, Zumin
AU - Li, Kefeng
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/6
Y1 - 2024/6
N2 - In current plant phenotyping research, the study of plant time-series images based on deep learning has received widespread attention. While such image data is relatively easy to obtain, the cost of annotation is high. One efficient method for achieving cost-effective training is through contrastive learning. Plant growth is slow, and the changes in image sequences over a period of time are small, with simple semantic information. Previous contrastive pre-training models struggled to effectively distinguish positive samples from the same image with different augmented views and similar negative samples from different images. Therefore, this paper proposes a method called self-supervised contrastive learning method for plant time-series images with a Priori Distance Embedding (PDE). The semantic information in images corresponding to different phenological stages of plants varies. This method transforms this crucial domain knowledge into prior distances for image pairs and conducts contrastive learning pre-training. The learned weights can be transferred to downstream tasks. Building upon this method, experiments were conducted on cherry time-series images to assess the quality of pre-training through a plant phenotyping image semantic segmentation task. To provide a comprehensive example of plant time-series image phenotypic analysis, this paper establishes a cherry growth temporal model, specifically including PDE pre-training, anomaly detection, semantic segmentation, and recording the results from the temporal dimension. The experiments indicate that this self-supervised contrastive learning method can be effectively applied to the pre-training of plant time-series images, demonstrating broad applicability in various computer vision studies related to plant phenotyping.
AB - In current plant phenotyping research, the study of plant time-series images based on deep learning has received widespread attention. While such image data is relatively easy to obtain, the cost of annotation is high. One efficient method for achieving cost-effective training is through contrastive learning. Plant growth is slow, and the changes in image sequences over a period of time are small, with simple semantic information. Previous contrastive pre-training models struggled to effectively distinguish positive samples from the same image with different augmented views and similar negative samples from different images. Therefore, this paper proposes a method called self-supervised contrastive learning method for plant time-series images with a Priori Distance Embedding (PDE). The semantic information in images corresponding to different phenological stages of plants varies. This method transforms this crucial domain knowledge into prior distances for image pairs and conducts contrastive learning pre-training. The learned weights can be transferred to downstream tasks. Building upon this method, experiments were conducted on cherry time-series images to assess the quality of pre-training through a plant phenotyping image semantic segmentation task. To provide a comprehensive example of plant time-series image phenotypic analysis, this paper establishes a cherry growth temporal model, specifically including PDE pre-training, anomaly detection, semantic segmentation, and recording the results from the temporal dimension. The experiments indicate that this self-supervised contrastive learning method can be effectively applied to the pre-training of plant time-series images, demonstrating broad applicability in various computer vision studies related to plant phenotyping.
KW - Contrastive learning
KW - Deep learning
KW - Plant phenotyping
KW - Plant temporal modeling
KW - Priori Distance Embedding (PDE)
UR - http://www.scopus.com/inward/record.url?scp=85191193301&partnerID=8YFLogxK
U2 - 10.1016/j.compag.2024.108973
DO - 10.1016/j.compag.2024.108973
M3 - Article
AN - SCOPUS:85191193301
SN - 0168-1699
VL - 221
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 108973
ER -