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相关概念视频

Light Acquisition02:16

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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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Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
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相关实验视频

Updated: Jul 25, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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使用深度学习和机器学习方法检测作物害虫的调查.

M Chithambarathanu1, M K Jeyakumar2

  • 1Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamilnadu India.

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|June 26, 2023
PubMed
概括

有效的害虫防治对于商业食品标准至关重要. 机器学习和深度学习提供先进的,自动化作物害虫检测,提高生产率和减少人类错误.

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:

  • 有效的害虫管理和控制对于商业食品标准至关重要,因为害虫对作物质量和生产率产生重大影响.
  • 由人类专家诊断作物异常,害虫或缺陷的传统方法昂贵且耗时.
  • 开发用于早期病虫病诊断的新工具对于防止重大作物损失至关重要.

研究的目的:

  • 通过机器学习和深度学习技术,提供近期农作物害虫和病原体识别研究的概述.
  • 突出用于农田自动监测害虫检测的现代方法.
  • 定义植物害虫检测方法,并对识别类,大米和棉花等作物害虫的方法进行分类.

主要方法:

  • 机器学习技术的审查,包括随机森林 (RF),支持矢量机器 (SVM),决策树 (DT) 和天真贝叶斯 (NB).
  • 探索深度学习方法,如卷积神经网络 (CNN),长期短期记忆 (LSTM),深度卷积神经网络 (DCNN) 和深度信念网络 (DBN).
  • 分析各种作物中植物害虫的自动监测和识别策略.

主要成果:

  • 机器学习和深度学习技术为农作物害虫检测提供了先进的解决方案.
  • 自动化监控系统减少了人为错误,减少了管理大型农业面积的努力.
关键词:
农业 农业 农业 农业深度学习是一种深度学习.识别大米害虫的识别方法机器学习是机器学习.对于类植物来说,有害生物的识别.对于棉花的农药识别方法

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  • 这些方法提高了作物保护,提高了作物的整体效率和生产力.

    • 结论:

    • 采用机器学习和深度学习的现代方法显著改善了作物害虫检测和管理.
    • 自动化虫害识别系统对于提高作物生产率和确保食品标准至关重要.
    • 审查的技术使得有效的监测,导致更好的作物保护和减少损失.