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 research team has 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.
The study, 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 sub-image dataset comprising 205 cropped soybean plant images.
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