通过计算深度学习彻底改变作物疾病检测:综合性综述
Habiba N Ngugi1, Absalom E Ezugwu2, Andronicus A Akinyelu3
1School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg, KwaZulu-Natal, 3201, South Africa.
Environmental monitoring and assessment
|February 24, 2024
概括
深度学习 (DL) 显著提高了农作物疾病检测的传统方法. 未来的研究应该专注于新兴的DL算法和针对各种作物疾病的统一框架.
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:
- 深度学习 (DL) 算法在作物检测和疾病识别方面优于传统方法.
- DL应用程序将植物图像转化为可用于早期疾病诊断的可操作的见解.
研究的目的:
- 提供关于作物疾病诊断,分类和严重程度评估的当代文献的全面审查.
- 在最近的研究中分析机器学习 (ML) 和DL技术的性能.
- 确定研究缺口,并为未来的调查提供建议.
主要方法:
- 关于ML和DL用于作物疾病诊断的当代文献的综述.
- 对各种ML和DL技术的性能分析,包括CNN,KNN,SVM和ANN.
- 对方法,数据集和研究缺口的审查.
主要成果:
- 大多数研究集中在传统的ML算法和CNN上,对囊网络和视觉转换器等新兴DL算法的探索有限.
- 现有的数据集通常是作物特定的,这突出了对多样化,全面的图像数据集的需求.
- 研究主要针对单个疾病或算法,而不是综合方法.
结论:
- 需要探索新兴的DL算法,并开发全面的数据集,以实现更广泛的应用.
- 为了有效应对多种植物疾病,建议将ML和DL结合在一起的统一框架.
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