LeafDNet:通过深度转移学习转变叶病诊断
Tofayet Sultan1, Mohammad Sayem Chowdhury1, Nusrat Jahan1
1Department of Computer Science American International University-Bangladesh Dhaka Bangladesh.
Plant direct
|February 13, 2025
概括
这项研究提出了一种先进的深度学习模型,用于精确检测,果和西红中的植物疾病. 这种基于Xception的新方法实现了高精度,有助于可持续农业和植物卫生管理.
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:
- 准确的植物病检测对于农业生产率至关重要.
- 传统的方法是劳动密集型的,经常不准确.
- 现代农业需要精确的,自动化的解决方案.
研究的目的:
- 开发一种先进的深度转移学习模型,用于识别植物疾病.
- 提高关键作物的疾病检测的准确性和效率.
- 为植物健康管理提供可扩展的解决方案.
主要方法:
- 使用了增强的Xception架构,并添加了额外的卷积和密集层.
- 整合了先进的规范化和退出技术以进行优化.
- 在四个疾病类别的5491张植物叶子图像数据集上训练并验证了模型.
主要成果:
- 实现了98%的准确性,99%的精度,98%的回忆和98%的F1分数.
- 与传统和其他深度学习方法相比,表现优越.
- 改进的模型有效地捕获了微妙的疾病模式.
结论:
- 拟议的深度学习框架为早期检测植物疾病提供了高度准确和高效的解决方案.
- 这项技术支持可持续的农业实践,并加强植物健康管理.
- 该模型显示了现实世界农业应用的巨大潜力.
相关概念视频
02:16Light Acquisition
8.4K
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.
8.4K
01:19Survival Tree
52
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
52