Welcome to Smart Agriculture 中文

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

• Special Issue--Agricultural Information Perception and Models • Previous Articles     Next Articles

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. 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
  • Foundation items:
    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)
  • About author:
    ZHANG Jianhua, E-mail:
    YAO Qiong, E-mail:
  • corresponding author:
    ZHOU Guomin, E-mail:

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