ArticlePlant methods2021
High-throughput soybean seeds phenotyping with convolutional neural networks and transfer learning.
Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.Plant phenomics (Washington, D.C.) · 2026Review
- PodNet: Pod real-time instance segmentation in pre-harvest soybean fields.Plant phenomics (Washington, D.C.) · 2025Article
- GrainNet: efficient detection and counting of wheat grains based on an improved YOLOv7 modeling.Plant methods · 2025Article
- Robust soybean seed yield estimation using high-throughput ground robot videos.Frontiers in plant science · 2025Article
- High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.Plants (Basel, Switzerland) · 2024Article
- Adherent Peanut Image Segmentation Based on Multi-Modal Fusion.Sensors (Basel, Switzerland) · 2024Article
- DEKR-SPrior: An Efficient Bottom-Up Keypoint Detection Model for Accurate Pod Phenotyping in Soybean.Plant phenomics (Washington, D.C.) · 2024Article
- Deep learning-based elaiosome detection in milk thistle seed for efficient high-throughput phenotyping.Frontiers in plant science · 2024Article
- Automatic Evaluation of Soybean Seed Traits Using RGB Image Data and a Python Algorithm.Plants (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis.Foods (Basel, Switzerland) · 2023Review
- Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.Frontiers in plant science · 2023Article
- A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers.Frontiers in plant science · 2023Article
- Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 Model.Foods (Basel, Switzerland) · 2022Article
- Banana plant counting and morphological parameters measurement based on terrestrial laser scanning.Plant methods · 2022Article
- Multi-Omics Techniques for Soybean Molecular Breeding.International journal of molecular sciences · 2022Review
- A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images.Sensors (Basel, Switzerland) · 2021Article
- DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics.Plant methods · 2021Article
- Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on wavelet neural network approach using spectral indices.Plant methods · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundEffective soybean seed phenotyping demands large-scale accurate quantities of morphological parameters. The traditional manual acquisition of soybean seed morphological phenotype information is error-prone, and time-consuming, which is not feasible for large-scale collection. The segmentation of individual soybean seed is the prerequisite step for obtaining phenotypic traits such as seed length and seed width. Nevertheless, traditional image-based methods for obtaining high-throughput soybean seed phenotype are not robust and practical. Although deep learning-based algorithms can achieve accurate training and strong generalization capabilities, it requires a large amount of ground truth data which is often the limitation step.
resultsWe showed a novel synthetic image generation and augmentation method based on domain randomization. We synthesized a plenty of labeled image dataset automatedly by our method to train instance segmentation network for high throughput soybean seeds segmentation. It can pronouncedly decrease the cost of manual annotation and facilitate the preparation of training dataset. And the convolutional neural network can be purely trained by our synthetic image dataset to achieve a good performance. In the process of training Mask R-CNN, we proposed a transfer learning method which can reduce the computing costs significantly by finetuning the pre-trained model weights. We demonstrated the robustness and generalization ability of our method by analyzing the result of synthetic test datasets with different resolution and the real-world soybean seeds test dataset.
conclusionThe experimental results show that the proposed method realized the effective segmentation of individual soybean seed and the efficient calculation of the morphological parameters of each seed and it is practical to use this approach for high-throughput objects instance segmentation and high-throughput seeds phenotyping.
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