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Updated: Jan 7, 2026

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

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全球大米多类细分数据集 (RiceSEG):用于开发和对大米细分算法的基准测试的全面和多样化的高分辨率RGB注释图像

Junchi Zhou1, Haozhou Wang2, Yoichiro Kato2

  • 1Engineering Research Center of Plant Phenotyping, Ministry of Education, Jiangsu Collaborative Innovation Center for Modern Crop Production, Sanya Institute of Nanjing Agricultural University, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China.

Plant phenomics (Washington, D.C.)
|December 19, 2025
PubMed
概括

创建了一个新的大米语义细分数据集,RiceSEG,以改善大米表型化中的计算机视觉. 这一数据集有助于开发精准农业和加速育种的先进模型,解决图像分析当前的挑战.

关键词:
农作物监测 农作物监测 农作物监测深度学习是一种深度学习.米的表型鉴定 米的表型鉴定米SEG数据集 米SEG数据集语义细分 语义细分是指语义细分.

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Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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