Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

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.

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

Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles.

Sensors (Basel, Switzerland)·2023

Same author

Secured and Privacy-Preserving Multi-Authority Access Control System for Cloud-Based Healthcare Data Sharing.

Sensors (Basel, Switzerland)·2023

Same author

IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review.

Healthcare (Basel, Switzerland)·2022

Same author

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.

International journal of environmental research and public health·2022

Same author

Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning.

Sensors (Basel, Switzerland)·2022

Same author

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning.

Sensors (Basel, Switzerland)·2022

Search research articles

相关实验视频

Updated: Jul 11, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

基于智能手机的番茄叶病检测系统使用EfficientNetV2B2及其与人工智能 (AI) 的解释性.

Anjan Debnath¹, Md Mahedi Hasan¹, M Raihan¹

¹Department of Computer Science and Engineering, North Western University, Khulna 9100, Bangladesh.

Sensors (Basel, Switzerland)

|November 14, 2023

概括

本研究引入了一种自动化系统,用于使用EfficientNetV2B2和深度学习 (DL) 检测西红植物疾病. 该系统达到近100%的准确性,有助于可持续农业和减少作物损失.

关键词:

有效的网V2B2 这是Grad-CAM.在 LIME 时代,剥离研究是剥离研究.深度学习是一种深度学习.可以解释的人工智能AI 智能手机的智能手机智能手机的智能手机.番茄叶子番茄叶子的叶子转移学习转移学习网络应用程序网络应用程序

更多相关视频

Author Spotlight: Advancing Stomatal Research with Automated Aperture Measurement

Author Spotlight: Advancing Stomatal Research with Automated Aperture Measurement

Published on: February 9, 2024

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

相关实验视频

Last Updated: Jul 11, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Author Spotlight: Advancing Stomatal Research with Automated Aperture Measurement

Author Spotlight: Advancing Stomatal Research with Automated Aperture Measurement

Published on: February 9, 2024

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

科学领域:

农业科学农业科学
计算机科学计算机科学
植物病理学植物病理学

背景情况:

番茄病导致大量的农业产量和经济损失.
及时发现疾病对于有效的管理和缓解至关重要.
早期检测可以提高产量,减少化学品的使用,并有利于国家经济.

研究的目的:

开发一种精确有效的自动化系统,用于识别各种番茄植物疾病.
分析番茄叶的图像,以诊断疾病.
创建一个用户友好的应用程序,以准确识别番茄叶病.

主要方法:

利用EfficientNetV2B2模型,一个深度学习 (DL) 架构,用于图像分类.
雇员转移学习 (TF) 具有先前存在的权重和256层密集层用于模型培训.
开发了健康和病变的西红叶的高分辨率图像数据集.

主要成果:

使用5倍交叉验证实现了99.02%的平均加权训练准确率和99.22%的平均加权验证准确率.
分解方法的结果是99.93%的训练准确率和100%的验证准确率.
深度学习方法在用于识别番茄叶病的测试数据集上显示了近100%的准确性.

结论:

开发的自动化系统准确诊断番茄叶病,使得可以做出明智的管理决策.
该系统通过快速疾病识别来支持可持续的番茄种植实践.
在智能手机和在线应用程序中部署为用户提供了可访问和准确的疾病诊断.