Intelligent Identification of Crop Agronomic Traits and Morphological Structure Phenotypes: A Review

doi:10.12133/j.smartag.SA202401015

Abstract

Abstract:

[Significance] The crop phenotype is the visible result of the complex interplay between crop genes and the environment. It reflects the physiological, ecological, and dynamic aspects of crop growth and development, serving as a critical component in the realm of advanced breeding techniques. By systematically analyzing crop phenotypes, researchers can gain valuable insights into gene function and identify genetic factors that influence important crop traits. This information can then be leveraged to effectively harness germplasm resources and develop breakthrough varieties. Utilizing data-driven, intelligent, dynamic, and non-invasive methods for measuring crop phenotypes allows researchers to accurately capture key growth traits and parameters, providing essential data for breeding and selecting superior crop varieties throughout the entire growth cycle. This article provides an overview of intelligent identification technologies for crop agronomic traits and morphological structural phenotypes. [Progress] Crop phenotype acquisition equipment serves as the essential foundation for acquiring, analyzing, measuring, and identifying crop phenotypes. This equipment enables detailed monitoring of crop growth status. The article presents an overview of the functions, performance, and applications of the leading high-throughput crop phenotyping platforms, as well as an analysis of the characteristics of various sensing and imaging devices used to obtain crop phenotypic information. The rapid advancement of high-throughput crop phenotyping platforms and sensory imaging equipment has facilitated the integration of cutting-edge imaging technology, spectroscopy technology, and deep learning algorithms. These technologies enable the automatic and high-throughput acquisition of yield, resistance, quality, and other relevant traits of large-scale crops, leading to the generation of extensive multi-dimensional, multi-scale, and multi-modal crop phenotypic data. This advancement supports the rapid progression of crop phenomics. The article also discusses the research progress of intelligent recognition technologies for agronomic traits such as crop plant height acquisition, crop organ detection, and counting, as well as crop ideotype recognition, crop morphological information measurement, and crop three-dimensional reconstruction for morphological structure intelligent recognition. Furthermore, this article outlines the main challenges faced in this field, including: difficulties in data collection in complex environments, high requirements for data scale, diversity, and preprocessing, the need to improve the lightweight nature and generalization ability of models, as well as the high cost of data collection equipment and the need to enhance practicality. [Conclusions and Prospects] Finally, this article puts forward the development directions of crop phenotype intelligent recognition technology, including: developing new and low cost intelligent field equipment for acquiring and analyzing crop phenotypes, enhancing the standardization and consistency of field crop phenotype acquisition, strengthening the generality of intelligent crop phenotype recognition models, researching crop phenotype recognition methods that involve multi-perspective, multimodal, multi-point continuous analysis, and spatiotemporal feature fusion, as well as improving model interpretability.

Key words: crop intelligent perception, phenotypic recognition, organ detection and technology, deep learning, 3D reconstruction, morphometry, large models

ZHANG Jianhua, YAO Qiong, ZHOU Guomin, WU Wendi, XIU Xiaojie, WANG Jian. Intelligent Identification of Crop Agronomic Traits and Morphological Structure Phenotypes: A Review[J]. Smart Agriculture, 2024, 6(2): 14-27.

Figures/Tables 5

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Table 1

Table 2

References 72

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编号	作者	作物种类	测量指标	重构方法	准确率R²/%
1	Ma 等^［51］	大豆	株高，叶面积指数	beer-lambert law	> 94.00
2	Wu等^［52］	小麦	株高，叶面积，投影面积，枝条体积和致密度	MVS-Pheno V2	> 96.00
3	Zhang等^［53］	玉米、大豆	叶片倾斜角度和方向测量	3D calibration approach	76.00、94.00
4	Ao等^［54］	玉米	茎位置精度，株高，冠宽和叶面积	PointCNN	> 85.00
5	Li等^［55］	玉米	叶长，叶宽，叶倾角，叶长高，株高和茎高	PointNet	> 82
6	Gong等^［56］	水稻	茎的直径，茎的长度，穗的长、高、宽，主穗，小穗	Panicle-3D	93.40
7	杨琳等^［57］	油菜	叶片，角果	超体素算法	93.38

编号	作者	采集设备	作物种类	重构方法	准确率R ²/%
1	Ma等^［66］	RGB相机	大豆	形状特征--相干点漂移（Intrinsic Shape Signatures-Coherent Point Drift， ISS-CPD）算法和迭代最接近点（Iterative Closest Point， ICP）算法	96.54
2	Li等^［67］	相机	玉米	欧氏聚类算法、颜色滤波算法和点云体素滤波算法	>92.60
3	魏天翔等^［68］	相机	水稻	DeepLabv3+	> 89.00
4	Zhu 等^［69］	相机	玉米	三维辐射模型	>97.00
5	史维杰^［70］	相机	谷子	运动恢复结构的稀疏重建、多视角立体几何	> 94.00
6	Ma等^［71］	三维激光扫描仪	玉米	网格法	>89.07
7	Sun等^［72］	相机	大豆	U-net	99.53

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