植物疾病的检测和分类:一个系统的文献审查
Ramanjot1, Usha Mittal1, Ankita Wadhawan1
1Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本综述分析了植物疾病检测和分类的技术,这对印度农业至关重要. 它确定了提高系统性能和确定作物疾病的准确性方法.
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:
- 农业对印度经济至关重要,但农作物产量受到气候变化加剧的植物疾病的威胁.
- 病原体和环境变化导致影响各种植物物种和农业生产力的疾病.
研究的目的:
- 分析现有的植物病检测和分类技术.
- 为研究人员提供数据驱动的资源,以提高系统性能和确定作物疾病的准确性.
主要方法:
- 2010年至2022年间发表的75篇同行评审论文的系统文献综述.
- 数据来源分析,预处理,特征提取,数据增强和分类模型.
- 评估图像增强和过拟合减少技术.
主要成果:
- 确定了各种基于数据的方法来检测植物疾病.
- 评估不同机器学习模型和图像处理技术的有效性.
- 突出了提高模型准确度和减少过度装配的方法.
结论:
- 现有的技术为准确检测和分类植物疾病提供了巨大的潜力.
- 进一步的研究可以利用这些数据驱动的方法来提高农业生产率和粮食安全.
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