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Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

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Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
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Reconstructing missing BOD<sub>5</sub> data from COD and its implications for water quality index assessment in a large irrigation system.

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Updated: Jun 17, 2025

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits
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Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

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使用混合机器学习算法识别种子纯度的一种新方法.

Thi-Thu-Hong Phan1, Quoc-Trinh Vo1, Huu-Du Nguyen2

  • 1Artificial Intelligence Department, FPT University, Da Nang, 550000, Vietnam.

Heliyon
|August 7, 2024
PubMed
概括

准确的水种子纯度识别对于谷物行业至关重要. 一种新的混合机器学习方法显著改善了种子纯度检测的现有方法.

科学领域:

  • 农业科学 农业科学
  • 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:

  • 种子的纯度对大米产量,营养含量和市场价格至关重要.
  • 目前的方法难以准确识别混合大米品种.
  • 确保大米种子的纯度可以最大限度地减少经济损失,并保持品种完整性.

研究的目的:

  • 开发一种自动化方法来识别特定的米品种纯度.
  • 提高谷物行业种子纯度分析的准确性和效率.
  • 为了应对在大米生产中混合种子品种的挑战.

主要方法:

  • 利用深度学习架构从原始种子数据中提取特征.
  • 采用混合机器学习算法来对大米种子进行可靠的分类.
  • 进行了广泛的实验,以验证拟议模型的性能.

主要成果:

  • 这种新的混合机器学习方法在与现有技术相比,表现出了更高的性能.
  • 在米种纯度识别的准确性方面取得了实质性的改进.
  • 验证了开发系统的实际适用性和有效性.

结论:

关键词:
功能提取 功能提取机器学习 机器学习这就是ResNet-50的特点.米种子纯度识别标识在VGG16中,VGG16是VGG16中的一个.

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  • 拟议的混合机器学习方法在自动化大米种子纯度检测方面取得了重大进展.
  • 这项技术有可能彻底改变大米谷物行业的质量控制.
  • 有效的水种子纯度识别系统对于优化农业产量和价值至关重要.