基于使用ConvNets转移学习的早期黑胡叶病预测
Anita S Kini1, K V Prema2, Smitha N Pai3
1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, India.
Scientific reports
|January 16, 2024
概括
这项研究引入了一种深度学习系统,用于早期检测黑胡叶病. 卷积神经网络模型实现了超过99%的准确性,有助于及时预防作物.
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:
- 农作物疾病对农业造成重大损害,需要早期检测方法.
- 黑胡是一种有价值的药用植物,易患各种叶病.
- 计算机视觉系统为及时诊断和预防疾病提供了潜力.
研究的目的:
- 开发一种智能转移学习技术,用于预测突出的黑胡叶病.
- 实施最先进的深度学习模型,特别是卷积神经网络,用于疾病识别.
- 通过早期和准确的疾病预测,增强农业实践.
主要方法:
- 利用转移学习与在ImageNet数据集上训练的深度神经网络.
- 开发了一个新的数据集实时黑胡叶图像,由专家注释.
- 培训和评估了多种深度学习模型,包括Inception V3,GoogleNet,SqueezeNet和Resnet18. 这些模型包括:
- 优化了诸如学习率,优化算法和批量大小等超参数.
主要成果:
- 在Resnet18模型实现了最高准确率99.67%.
- 所有评估的模型都表现出高的验证准确性,从99.1%到99.7%不等.
- 在所有模型中观察到较低的验证损失,表明有效的学习.
结论:
- 拟议的深度学习方法显著改善了黑早期叶病的识别.
- 这种尖端的方法为农业疾病管理提供了有价值的工具.
- 准确和及时的疾病预测可以导致有针对性的预防策略和减少作物损失.
相关概念视频
02:16Light Acquisition
8.5K
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.
8.5K
01:19Survival Tree
86
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...
86