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Light Acquisition

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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相关实验视频

Updated: Jun 4, 2025

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

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

Published on: August 8, 2017

使用高光谱成像技术和机器学习分析检测梨质量.

Zishen Zhang^1,2,3, Hong Cheng^2,3, Meiyu Chen^2,3,4

¹College of Horticulture, Xinjiang Agricultural University, Urumqi 830052, China.

Foods (Basel, Switzerland)

|December 17, 2024

概括

超光谱成像与机器学习相结合,提供了一种高效,非破坏性的方法来评估梨的质量. 这项技术准确地预测了度,可溶性固体和成熟度,增强了农业和食品工业的应用.

关键词:

超光谱成像技术的使用.机器学习模型机器学习模型不具有破坏性的检测检测.梨子梨子梨子预测模型预测模型

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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

相关实验视频

Last Updated: Jun 4, 2025

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

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

Published on: August 8, 2017

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

科学领域:

农业科学农业科学
食品科学食品科学食品科学
频谱学是一种光谱学.

背景情况:

非破坏性水果质量检测对于农业和食品行业至关重要.
超光谱成像 (HSI) 技术为快速,非破坏性质量评估提供了一个有希望的方法.

研究的目的:

应用高光谱成像 (HSI) 和机器学习来有效评估梨的质量.
开发一种强大的技术方法,用于对梨的关键质量参数进行非破坏性分析.

主要方法:

使用高光谱数据 (398-1004 nm) 分析了六种梨品种.
最小方形支持向量机 (LS-SVM) 模型通过预处理 (FD-SNV) 和特征选择 (CARS) 进行了优化.
逆向传播神经网络 (BPNN) 用于品种分类.

主要成果:

通过FD-SNV预处理和CARS特征选择,LS-SVM模型的性能在预测度,SSC,pH,颜色和成熟度方面得到了显著的改进.
整合了多种梨品种 (n=6) 的数据,结果是RPD>2.0,表明模型性能强大.
该BPNN模型在区分六种梨品种方面实现了>99%的准确性.

结论:

在HSI和机器学习的结合下,提供了一种高效,快速和非破坏性的方法来检测梨的质量.
这种方法对于水果行业的质量控制和商业加工具有实际价值.
优化的预处理和特征选择提高了预测准确度,降低了计算负载.