Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

19.8K
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.
19.8K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Latent layers in social networks and their implications for comparative analyses.

Behavioral ecology : official journal of the International Society for Behavioral Ecology·2025
Same author

Ecology needs a causal overhaul.

Biological reviews of the Cambridge Philosophical Society·2025
Same author

Tailoring convolutional neural networks for custom botanical data.

Applications in plant sciences·2025
Same author

Computer vision for plant pathology: A review with examples from cocoa agriculture.

Applications in plant sciences·2024
Same author

The evolution of menopause in toothed whales.

Nature·2024
Same author

BISoN: A Bayesian Framework for Inference of Social Networks.

Methods in ecology and evolution·2024

相关实验视频

Updated: Sep 17, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K

通过先进的培训技术,改善植物病理学的计算机视觉.

Jamie R Sykes1, Katherine J Denby2, Daniel W Franks3

  • 1Department of Computer Science University of York, Deramore Lane York YO10 5GH Yorkshire United Kingdom.

Applications in plant sciences
|June 27, 2025
PubMed
概括
此摘要是机器生成的。

先进的培训技术,如半监督学习和动态焦点损失显著提高卷积神经网络性能可可病检测. 通过这些方法,ResNet18显示出对现实世界农业应用的巨大潜力.

关键词:
计算机视觉 计算机视觉疾病检测检测疾病检测机器学习是机器学习.半监督学习 半监督学习

更多相关视频

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform
06:28

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

Published on: June 7, 2024

2.0K
Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

1.1K

相关实验视频

Last Updated: Sep 17, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K
Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform
06:28

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

Published on: June 7, 2024

2.0K
Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

1.1K

科学领域:

  • 农业科学 农业科学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 卷积神经网络 (CNN) 对于可可 (Theobroma cacao) 的疾病检测至关重要.
  • 最近在CNN图像分类准确度方面的进展已经停滞不前.
  • 提高模型的通用性和稳定性对于现实世界农业疾病管理至关重要.

研究的目的:

  • 调查高级培训技术,以提高CNN在可可病检测中的性能.
  • 为了解决图像分类的计算机视觉准确度改善的停滞.
  • 为农业应用开发更强大,更普遍的深度学习模型.

主要方法:

  • 雇佣半监督学习以减少过度拟合和增强通用性.
  • 引入了一个非可可类类,以使模型暴露在各种特征中,提高了强度.
  • 开发并利用动态焦点损失,一种新的损失函数,根据经验难度对图像进行权重.
  • 使用Grad-CAM进行对模型行为进行定性评估.

主要成果:

  • 半监督学习显著改善了对微妙疾病症状的表现.
  • 在具有挑战性的情况下,加入非可可类增加了模型的稳定性.
  • 动态焦点损失为难以处理的图像提供了更好的处理.
  • ResNet18与半监督学习和动态焦点损失相结合,在实际部署中表现最强.

结论:

  • 半监督学习和高级损失函数在改善农业疾病管理中的深度学习方面具有重大潜力.
  • 该研究引入了一套新的,高质量的基准数据集,包括7220张可可病检测图像,提出了一个更现实的挑战.
  • 开发的方法为农业中更有效,更可靠的自动化疾病检测系统提供了途径.