物联网和深度学习技术在植物疾病检测和分类中的作用:集中审查
Vijaypal Singh Dhaka1, Nidhi Kundu2, Geeta Rani1
1Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, India.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本综述探讨使用物联网 (IoT) 和深度学习来检测植物疾病. 整合这些技术可以通过自动化图像分析和作物疾病分类来增强精密农业.
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:
- 自动植物疾病检测对于精确和智能农业至关重要.
- 物联网 (IoT) 在数据收集方面表现出色,而深度学习模型则自动提取功能.
- 整合物联网和深度学习为作物疾病监测和分类提供了强大的解决方案.
研究的目的:
- 为植物疾病监测和分类提供物联网和深度学习模型的全面审查.
- 分析现有技术的优势,局限性和研究差距.
- 在公共数据集上比较各种深度学习模型的性能.
主要方法:
- 对物联网和植物病理学深度学习应用的系统文献综述.
- 分析不同的深度学习架构 (例如,CNN,RNN) 和它们的适用性.
- 基于数据集大小,响应时间和计算资源的模型性能评估.
主要成果:
- 确定了当前植物疾病分类方法中的关键研究缺陷.
- 在不同数据集中比较各种深度学习模型的性能指标.
- 提供了基于实际约束的最佳深度学习模型选择的见解.
结论:
- 物联网和深度学习的整合对于推进自动化植物疾病管理至关重要.
- 了解模型性能权衡对于开发高效和有效的解决方案至关重要.
- 本次审查指导了优化和混合模型的开发,以准确地分类植物疾病.
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