DEKR-SPrior:用于精确大豆花苞表型的高效自下而上关键点检测模型

IF 7.6 1区 农林科学 Q1 AGRONOMY
Plant Phenomics Pub Date : 2024-06-27 eCollection Date: 2024-01-01 DOI:10.34133/plantphenomics.0198
Jingjing He, Lin Weng, Xiaogang Xu, Ruochen Chen, Bo Peng, Nannan Li, Zhengchao Xie, Lijian Sun, Qiang Han, Pengfei He, Fangfang Wang, Hui Yu, Javaid Akhter Bhat, Xianzhong Feng
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引用次数: 0

摘要

豆荚数和种子数是大豆与产量相关的重要性状。高精度大豆育种人员面临的主要挑战是如何以高通量方式准确地对豆荚和种子数量进行表型。人工智能领域的最新进展,尤其是深度学习(DL)模型,为高通量、更精确的作物性状表型提供了新途径。然而,现有的深度学习模型对于原位大豆植株中密集重叠的豆荚表型效果较差;因此,准确表型大豆植株中的豆荚和种子数量是一项重要挑战。为解决这一难题,本研究提出了一种自下而上的大豆原位结荚表型模型 DEKR-SPrior(带结构先验的分离关键点回归),该模型将大豆的荚果和种子分别类比为人类的 "人 "和 "关节"。特别是,我们设计了一个新颖的结构先验(SPrior)模块,利用余弦相似性提高特征判别能力,这对于区分位置接近的种子和高度相似的种子非常重要。为了进一步提高豆荚定位的准确性,我们将全尺寸图像裁剪成更小的高分辨率子图像进行分析。对图像数据集的研究结果表明,DEKR-SPrior 的表现优于多种自下而上模型,即轻量级-OpenPose、OpenPose、HigherHRNet 和 DEKR,在豆荚表型分析中将平均绝对误差从 25.81(原始 DEKR)降低到 21.11(DEKR-SPrior)。本文展示了 DEKR-SPrior 在植物表型分析中的巨大潜力,我们希望 DEKR-SPrior 能为未来的植物表型分析提供帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DEKR-SPrior: An Efficient Bottom-Up Keypoint Detection Model for Accurate Pod Phenotyping in Soybean.

The pod and seed counts are important yield-related traits in soybean. High-precision soybean breeders face the major challenge of accurately phenotyping the number of pods and seeds in a high-throughput manner. Recent advances in artificial intelligence, especially deep learning (DL) models, have provided new avenues for high-throughput phenotyping of crop traits with increased precision. However, the available DL models are less effective for phenotyping pods that are densely packed and overlap in in situ soybean plants; thus, accurate phenotyping of the number of pods and seeds in soybean plant is an important challenge. To address this challenge, the present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping, which considers soybean pods and seeds analogous to human people and joints, respectively. In particular, we designed a novel structural prior (SPrior) module that utilizes cosine similarity to improve feature discrimination, which is important for differentiating closely located seeds from highly similar seeds. To further enhance the accuracy of pod location, we cropped full-sized images into smaller and high-resolution subimages for analysis. The results on our image datasets revealed that DEKR-SPrior outperformed multiple bottom-up models, viz., Lightweight-OpenPose, OpenPose, HigherHRNet, and DEKR, reducing the mean absolute error from 25.81 (in the original DEKR) to 21.11 (in the DEKR-SPrior) in pod phenotyping. This paper demonstrated the great potential of DEKR-SPrior for plant phenotyping, and we hope that DEKR-SPrior will help future plant phenotyping.

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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: 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. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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