Abstract: Plant phenotyping captures the integrated structural and functional traits of crops across cellular, tissue, organ, whole-plant, and population scales. It represents the outward expression of genotype–environment interactions and provides essential technological support for precision breeding, smart agriculture, and sustainable crop production. As farming shifts from experience-based to data-driven decision-making, the efficient acquisition and integrated analysis of phenotypic information at multiple spatial scales has emerged as a major research frontier at the intersection of agronomy, plant science, and agricultural engineering. Recent advances in high-throughput phenotyping platforms, unmanned aerial vehicles (UAVs), satellite remote sensing, and artificial intelligence have propelled the field from single-scale observations toward multi-scale collaborative analysis. This cross-scale integration offers powerful tools for dissecting genotype–environment–management interactions and accelerates the translation of phenotypic insights into practical agricultural decisions. Against this backdrop, the present special issue—entitled “AI in Plant Phenotyping: From Cells to Fields”—features seven original research papers that span organ/plant, plot/field, and regional/large-field scales.

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[21]YaoJ, KeXB, GuXY, et al., 2026. Optimized substrate selection for enhanced orchid growth based on high-throughput lysimetric arrays. J Zhejiang Univ-Sci B, 27(5):437-449.

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[24]ZhangW, RenY, GuoZD, et al., 2026. Improved lightweight convolutional neural network models for the detection and evaluation of fusarium head blight in wheat. J Zhejiang Univ-Sci B, 27(5):450-465.

[25]ZhangYN, ChaiXY, HuJP, et al., 2026. Enhancing rapeseed biomass and yield estimation with ensemble learning and synergistic multidimensional features. J Zhejiang Univ-Sci B, 27(5):499-516.

[26]ZhaoJT, LiZH, BaiB, et al., 2026. A novel deep learning framework for High-Throughput peanut seedling identification across diverse germplasm and complex field environments. Int J Appl Earth Obs Geoinf, 146:105061.

[27]ZhaoYJ, LiuDJ, WangZ, et al., 2025. Automated phenotyping of maize from 3D point clouds using an optimized deep learning approach. Agriculture, 15(23):2430.

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