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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[3]DongH, XiaYL, HuRQ, et al., 2025. Improvement of crop growth simulations under different drip irrigation modes by jointly assimilating UAV multimodal data into crop models. Agric For Meteorol, 375:110870.

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[7]LiJJ, ZhuKY, ZhangQW, et al., 2026. Object-centric 3D Gaussian splatting for strawberry plant reconstruction and phenotyping. Smart Agric Technol, 13:101810.

[8]LiT, LiuY, FengHK, et al., 2026. RCTUnet: a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction. J Zhejiang Univ-Sci B, 27(5):517-536.

[9]MuXY, LuYZ, 2025. Non-destructive detection of spotted wing Drosophila infestation in blueberry fruit using hyperspectral imaging technology. Agric Commun, 3(3):100096.

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[12]SadehR, AlchanatisV, Ben-DavidR, et al., 2025. UAV-borne hyperspectral and thermal imagery integration empowers genetic dissection of wheat stomatal conductance. Comput Electron Agric, 235:110411.

[13]SaifMS, ChanciaR, MurphySP, et al., 2026. Advancing table beet root yield estimation via unmanned aerial systems (UAS) multi-modal sensing. ISPRS J Photogramm Remote Sens, 232:542-560.

[14]ShojaeezadehSA, ElnasharA, David WeberTK, 2025. A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning. Sci Remote Sens, 11:100227.

[15]TanakaTST, WangS, JørgensenJR, et al., 2024. Review of crop phenotyping in field plot experiments using UAV-mounted sensors and algorithms. Drones, 8(6):212.

[16]TanakaTST, GislumR, 2025. Prediction of winter wheat nitrogen status using UAV imagery, weather data, and machine learning. Eur J Agron, 164:127534.

[17]TaoJX, LiXL, ZhangJF, et al., 2026. Improving RGB image recognition in the YOLO11n algorithm for accurate detection of tea plant diseases. J Zhejiang Univ-Sci B, 27(5):482-498.

[18]WangCL, ChiJN, ZhangX, et al., 2026. Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series. J Zhejiang Univ-Sci B, 27(5):537-560.

[19]WilliamsD, MacfarlaneF, BrittenA, 2024. Leaf only SAM: a segment anything pipeline for zero-shot automated leaf segmentation. Smart Agric Technol, 8:100515.

[20]YanPC, FengYM, HanQS, et al., 2025. Revolutionizing crop phenotyping: enhanced UAV LiDAR flight parameter optimization for wide-narrow row cultivation. Remote Sens Environ, 320:114638.

[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.

[22]ZhangJY, GuanKY, ChenZL, et al., 2025. Aligning satellite-based phenology in a deep learning model for improved crop yield estimates over large regions. Agric For Meteorol, 372:110675.

[23]ZhangWQ, DangLM, NguyenLQ, et al., 2024. Adapting the segment anything model for plant recognition and automated phenotypic parameter measurement. Horticulturae, 10(4):398.

[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.