作物农艺性状与形态结构表型智能识别技术综述

doi:10.12133/j.smartag.SA202401015

Smart Agriculture ›› 2024, Vol. 6 ›› Issue (2): 14-27.doi: 10.12133/j.smartag.SA202401015

• 专刊--农业信息感知与模型 • 上一篇下一篇

作物农艺性状与形态结构表型智能识别技术综述

张建华¹^,², 姚琼¹^,³(), 周国民¹^,²^,⁶(), 吴雯迪¹^,⁴, 修晓杰¹^,⁵, 王健¹^,²

^1. 三亚中国农业科学院国家南繁研究院，海南三亚 572024，中国
^2. 中国农业科学院农业信息研究所/国家农业科学数据中心，北京 100081，中国
^3. 河南大学农学院，河南开封 475004，中国
^4. 海南大学热带农林学院，海南海口 570228，中国
^5. 杭州科技职业技术学院物联网技术学院，浙江杭州 311403，中国
^6. 农业农村部南京农业机械化研究所，江苏南京 210014，中国

收稿日期:2024-01-13 出版日期:2024-03-30
基金资助:
三亚崖州湾科技城科技专项(SCKJ-JYRC-2023-45); 三亚中国农业科学院国家南繁研究院南繁专项(YBXM2409;YBXM2410;YBXM2312;ZDXM2311); 国家重点研发计划(2022YFF0711805;2022YFF0711801); 中央级公益性科研院所基本科研业务费专项(JBYW-AII-2024-05;JBYW-AII-2023-06); 中国农业科学院科技创新工程(CAAS-ASTIP-2024-AII;CAAS-ASTIP-2023-AII); 浙江省教育厅科研项目(Y202248622)
作者简介:
张建华，研究方向为视觉感知与智慧农业。E-mail：zhangjianhua@caas.cn；
姚琼，研究方向为作物表型智能分析。E-mail：yaoqiong24@163.com
张建华和姚琼对本文有同等贡献，并列第一作者。
通信作者:
周国民，博士，研究员，研究方向为数字农业与数据挖掘。E-mail：zhouguomin@caas.cn

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

ZHANG Jianhua¹^,², YAO Qiong¹^,³(), ZHOU Guomin¹^,²^,⁶(), WU Wendi¹^,⁴, XIU Xiaojie¹^,⁵, WANG Jian¹^,²

^1. National Academy of Southern Breeding of Sanya, Chinese Academy of Agricultural Sciences, Sanya 572024, China
^2. Agricultural Information Institute of Chinese Academy of Agricultural Sciences/National Agricultural Science Data Center, Beijing 10081, China
^3. Agricultural College, Henan University, Kaifeng 475004, China
^4. College of Tropical Crops, Hainan University, Haikou 570228, China
^5. School of Internet of Things technology, Hangzhou Polytechnic, Hangzhou 311404, China
^6. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China

Received:2024-01-13 Online:2024-03-30
corresponding author:
ZHOU Guomin, E-mail: zhouguomin@caas.cn
About author:
ZHANG Jianhua, E-mail: zhangjianhua@caas.cn
YAO Qiong, E-mail: yaoqiong24@163.com
Supported by:
Sanya Yazhou Bay Science and Technology City(SCKJ-JYRC-2023-45); Sanya Chinese Academy of Agricultural Sciences National South Breeding Research Institute South Breeding Special Project(YBXM2409;YBXM2410;YBXM2312;ZDXM2311); National Key Research and Development Plan(2022YFF0711805;2022YFF0711801); Central Public-interest Scientific Institution Basal Research Fund(JBYW-AII-2024-05;JBYW-AII-2023-06); Agricultural Science and Technology Innovation Project of CAAS(CAAS-ASTIP-2024-AII;CAAS-ASTIP-2023-AII); Scientific Research Project of Zhejiang Provincial Education Department(Y202248622)

摘要/Abstract

摘要：

[目的/意义] 作物农艺性状与形态结构表型智能识别是作物智慧育种的主要内容，是研究“基因型—环境型—表型”相互作用关系的基础，对现代作物育种具有重要意义。［进展］ 大规模、高通量作物表型获取设备是作物表型获取、分析、测量、识别等的基础和重要手段。本文介绍了高通量作物表型主流平台和感知成像设备的功能、性能以及应用场景。分析了作物株高获取、作物器官检测与技术等农艺性状智能识别和作物株型识别、作物形态信息测量以及作物三维重建等形态结构智能识别技术的研究进展及挑战。［结论/展望］从研制新型低成本田间智能作物表型获取与分析装备、提升作物表型获取田间环境的标准化与一致性水平、强化田间作物表型智能识别模型的通用性，研究多视角、多模态、多点连续分析与时空特征融合的作物表型识别方法，以及提高模型解释性等方面，展望了作物表型技术主要发展方向。

关键词: 作物智能感知, 表型识别, 器官检测与技术, 深度学习, 三维重建, 形态测量, 大模型

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

张建华, 姚琼, 周国民, 吴雯迪, 修晓杰, 王健. 作物农艺性状与形态结构表型智能识别技术综述[J]. 智慧农业(中英文), 2024, 6(2): 14-27.

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.

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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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作物农艺性状与形态结构表型智能识别技术综述

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

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