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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

张建华 姚琼 周国民 吴雯迪 修晓杰 王健

智慧农业(中英文)2024,Vol.6Issue(2):14-27,14.
智慧农业(中英文)2024,Vol.6Issue(2):14-27,14.DOI:10.12133/j.smartag.SA202401015

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

张建华 1姚琼 2周国民 3吴雯迪 4修晓杰 5王健1

作者信息

  • 1. 三亚中国农业科学院国家南繁研究院,海南三亚 572024,中国||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
  • 2. 三亚中国农业科学院国家南繁研究院,海南三亚 572024,中国||河南大学 农学院,河南开封 475004,中国
  • 3. 三亚中国农业科学院国家南繁研究院,海南三亚 572024,中国||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||农业农村部南京农业机械化研究所,江苏南京 210014,中国
  • 4. 三亚中国农业科学院国家南繁研究院,海南三亚 572024,中国||海南大学 热带农林学院,海南海口 570228,中国
  • 5. 三亚中国农业科学院国家南繁研究院,海南三亚 572024,中国||杭州科技职业技术学院 物联网技术学院,浙江杭州 311403,中国
  • 折叠

摘要

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 ad-vanced 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 germ-plasm resources and develop breakthrough varieties.Utilizing data-driven,intelligent,dynamic,and non-invasive methods for measur-ing 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 identify-ing crop phenotypes.This equipment enables detailed monitoring of crop growth status.The article presents an overview of the func-tions,performance,and applications of the leading high-throughput crop phenotyping platforms,as well as an analysis of the charac-teristics of various sensing and imaging devices used to obtain crop phenotypic information.The rapid advancement of high-through-put 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 dis-cusses the research progress of intelligent recognition technologies for agronomic traits such as crop plant height acquisition,crop or-gan detection,and counting,as well as crop ideotype recognition,crop morphological information measurement,and crop three-di-mensional 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 collec-tion equipment and the need to enhance practicality. [Conclusions and Prospects]Finally,this article puts forward the development directions of crop phenotype intelligent recognition tech-nology,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 rec-ognition 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.

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

Key words

crop intelligent perception/phenotypic recognition/organ detection and technology/deep learning/3D reconstruction/morphometry/large models

分类

信息技术与安全科学

引用本文复制引用

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):14-27,14.

基金项目

三亚崖州湾科技城科技专项(SCKJ-JYRC-2023-45) (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) (YBXM2409,YBXM2410,YBXM2312,ZDXM2311)

国家重点研发计划(2022YFF0711805,2022YFF0711801) (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) (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) (CAAS-ASTIP-2024-AII,CAAS-ASTIP-2023-AII)

浙江省教育厅科研项目(Y202248622) Sanya Yazhou Bay Science and Technology City(SCKJ-JYRC-2023-45) (Y202248622)

Sanya Chinese Academy of Agricultural Sciences National South Breeding Research Institute South Breeding Special Project(YBXM2409,YBXM2410,YBXM2312,ZDXM2311) (YBXM2409,YBXM2410,YBXM2312,ZDXM2311)

National Key Research and Development Plan(2022YFF0711805,2022YFF0711801) (2022YFF0711805,2022YFF0711801)

Central Public-interest Scientif-ic Institution Basal Research Fund(JBYW-AII-2024-05,JBYW-AII-2023-06) (JBYW-AII-2024-05,JBYW-AII-2023-06)

Agricultural Science and Technology Innovation Proj-ect of CAAS(CAAS-ASTIP-2024-AII,CAAS-ASTIP-2023-AII) (CAAS-ASTIP-2024-AII,CAAS-ASTIP-2023-AII)

Scientific Research Project of Zhejiang Provincial Education De-partment(Y202248622) (Y202248622)

智慧农业(中英文)

OACSTPCD

2096-8094

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