A research team developed the DEKR-SPrior model to improve high-throughput phenotyping of soybean pods and seeds. This model, which enhances feature discrimination through a novel SPrior module, significantly reduces the mean absolute error in pod phenotyping compared to existing models. DEKR-SPrior’s ability to accurately count and locate densely packed pods and seeds promises to streamline soybean breeding processes, offering a valuable tool for enhancing crop yield predictions and advancing agricultural research.
Soybean is an agriculturally important legume rich in protein and oil, with breeders aiming to enhance yields through traits like seed weight, shape, and pod count. Current research leverages deep learning (DL) for high-throughput phenotyping, yet conventional methods are laborious and error-prone. Segmentation-based and detection-based DL methods face challenges with dense, overlapping pods. To address these issues, the focus has shifted to exploring point-based detection methods, such as P2PNet, to accurately phenotype soybean pods and seeds in situ.
A study (DOI: 10.34133/plantphenomics.0198) published in Plant Phenomics on 27 Jun 2024, proposes the DEKR-SPrior model, incorporating structural prior knowledge, to improve the accuracy of soybean pod phenotyping.
In this study, the performance of the DEKR-SPrior model was compared with four other bottom-up models—Lightweight-OpenPose, OpenPose, HigherHRNet, and the original DEKR, on a high-resolution subimage dataset comprising 205 cropped soybean plant images. DEKR-SPrior demonstrated superior accuracy, with AP, AP50, AP(1-seeded), AP(2-seeded), AP(3-seeded), and AP(4-seeded) values of 72.4%, 91.4%, 71.7%, 80.9%, 85.6%, and 83.6%, respectively. Compared to the original DEKR, DEKR-SPrior showed notable improvements across all metrics, particularly with significant gains in AP for 2-seeded and 3-seeded pods. Precision-recall (PR) curves indicated that DEKR-SPrior maintained higher precision at given recall rates, effectively reducing missed and incorrect detections. The visualization of results showed accurate identification and connection of seed positions, even in densely packed pods. Ablation analysis further confirmed the enhancement provided by the SPrior module, with optimal performance achieved at a specific hyperparameter value. DEKR-SPrior also outperformed other models in full-sized image tests, achieving lower mean absolute errors (MAE) and higher Pearson correlation coefficients (PCC) for both seed and pod counts, underscoring its efficacy in soybean phenotyping.
According to the study’s lead researcher, Jingjing He, “This paper demonstrated the great potential of DEKR-SPrior for plant phenotyping, and we hope that DEKR-SPrior will help future plant phenotyping.”
In summary, the DEKR-SPrior model achieved higher precision and recall rates, demonstrating its effectiveness in accurately detecting and counting soybean pods and seeds. Looking to the future, DEKR-SPrior holds great potential for advancing agricultural research and breeding programs by providing a more accurate and efficient method for phenotyping crop traits. This model could be further refined and adapted for other crops, enhancing yield prediction and contributing to food security.
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References
DOI
10.34133/plantphenomics.0198
Original Source URL
https://spj.science.org/doi/10.34133/plantphenomics.0198
Authors
Jingjing He1*, Lin Weng 1, Xiaogang Xu 2, Ruochen Chen1, Bo Peng1,Nannan Li 1, Zhengchao Xie 1, Lijian Sun1, Qiang Han1 , Pengfei He1,Fangfang Wang 1, Hui Yu 3, Javaid Akhter Bhat 1, and Xianzhong Feng3
Affications
1Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China.
2School of Computer Science and Technology,Zhejiang Gongshang University, Hangzhou 310012, Zhejiang, China.
3 Key Laboratory of SoybeanMolecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy ofSciences, Changchun 130102, Jilin, China.
Funding information
This work was supported in part by the National Key Research and Development Program of China (2023YFD1202600), the National Natural Science Foundation of China (62103380), the Research and Development Project from the Department of Science and Technology of Zhejiang Province (2023C01042), Soybean Intelligent Computational Breeding and Application of the Zhejiang Lab (2021PE0AC04), Intelligent Technology and Platform Development for Rice Breeding of the Zhejiang Lab (2021PE0AC05), and Fine-grained Semantic Modeling and Cross-modal Encoding–Decoding for Multilingual Scene Text Extraction (2022M722911).
About Plant Phenomics
Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal’s Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
