Artificial Intelligence in Crop Phenotyping: Advances and Challenges

doi:10.11983/CBB25228

Abstract

Abstract: Focusing on the core issue of crop phenotyping empowered by artificial intelligence (AI), this paper systematically summarizes the typical biological characteristics of various crop phenotypes and the corresponding high-throughput phenotyping acquisition techniques from six dimensions, namely morphological structure, growth and development, physiology and biochemistry, yield and quality, stress and disease resistance, and molecular genetics. Aiming at the above six dimensions, it emphatically reviews the latest research findings and application progress of AI technologies in the automatic identification of plant phenotypic characteristics, the accurate extraction of multi-scale phenotypic parameters, and the modeling and analysis of multi-source phenotypic data. Finally, it analyzes the key challenges and deficiencies faced by current AI-driven crop phenotyping research in terms of adaptability to complex field environments, fusion of multi-source heterogeneous data, and accurate genotype-to-phenotype (G2P) modeling. Furthermore, it prospects the future development trends of AI technologies in the fields of crop phenotyping acquisition, analysis and phenomics, as well as their application prospects and research directions in intelligent breeding and agricultural intelligent decision-making.

Key words: artificial intelligence, crop phenotyping, crop phenomics, high-throughput phenotyping, smart agriculture

Meili Wang, Fangbo Lu, Wanchuang Luo, Yao Chen, Yong’an Yin, Jing Zhao, Ni Jiang, Hao Jiang, Shuqin Gao. Artificial Intelligence in Crop Phenotyping: Advances and Challenges[J]. Chinese Bulletin of Botany, 2026, 61(4): 549-714.

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URL: https://www.chinbullbotany.com/EN/10.11983/CBB25228

https://www.chinbullbotany.com/EN/Y2026/V61/I4/549

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