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相关概念视频

Light Acquisition02:16

Light Acquisition

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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 3, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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用机器视觉和机器学习算法预测玉米核的断裂率.

Chenlong Fan1, Wenjing Wang1, Tao Cui2

  • 1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Foods (Basel, Switzerland)
|January 8, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种机器视觉和机器学习方法,用于快速在线检测破碎的玉米粒. 这种方法可以准确预测核损伤率,防止收获期间的真菌污染.

关键词:
破碎的利率率是什么意思机械收割机 机械收割机 机械收割机检测 检测 检测 检测 检测图像处理是图像处理的过程.玉米粒玉米粒的种子

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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography
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Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

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科学领域:

  • 农业工程 农业工程
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 玉米收获需要快速检测破碎的玉米粒,以防止真菌损害.
  • 评估核损伤的现有方法往往是低效和主观的.

研究的目的:

  • 开发一种准确和客观的在线检测破碎玉米核的方法.
  • 引导玉米收获实践,以尽量减少玉米核损伤和减少真菌污染.

主要方法:

  • 构建了高湿度玉米核表型特征的数据集,提取了七个几何和形状特征.
  • 开发了回归模型,使用机器学习算法预测破碎和不破碎的内核重量.
  • 使用机器学习进行内核缺陷检测的既定分类模型.

主要成果:

  • 轻度梯度提升机 (LGBM) 和随机森林 (RF) 算法显示了高准确度 (r值为0.985和0.910) 的重量预测.
  • 支持矢量机 (SVM) 在分类内核缺陷方面实现了超过95%的准确性.
  • 预测和实际破碎率之间确实存在强烈的线性关系.

结论:

  • 拟议的机器视觉和机器学习方法提供了一个准确,客观和高效的方法,用于在线检测玉米中断率.
  • 这项技术可以显著提高玉米收获质量,减少收获后的损失.