植物病理学的计算机视觉:以可可农业为例的综述
Jamie R Sykes1, Katherine J Denby2, Daniel W Franks1,3
1Department of Computer Science University of York Deramore Lane, York YO10 5GH Yorkshire United Kingdom.
Applications in plant sciences
|April 19, 2024
概括
计算机视觉和机器学习提供精确的植物疾病检测,提高作物产量,减少滥用农药. 本综述详细介绍了农业有效应用的方法.
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:
- 植物疾病导致大量的作物损失,影响粮食安全和经济.
- 准确及时检测疾病对于有效的作物管理至关重要.
- 目前的方法在精度,成本和抗农药性潜力方面面临挑战.
研究的目的:
- 审查计算机视觉和机器学习用于检测植物疾病的应用和发展.
- 讨论在植物病理学中应用计算机视觉的标准方法之外的技术考虑.
- 为农业环境中实施这些技术提供指南.
主要方法:
- 计算机视觉和机器学习技术的审查,包括卷积神经网络 (CNN) 和变压器.
- 分析数据采集策略,生物诊断工具和特征检查.
- 探索支持矢量机器和进化的神经网络等方法.
- 适应标准计算机视觉方法的案例研究.
主要成果:
- 计算机视觉能够精确检测疾病,减少劳动力成本和错误诊断.
- 有效的应用将杀虫剂的滥用最小化,减轻财务损失,耐药性和污染.
- 审查强调了各种机器学习模型的优势和数据策划的好处.
结论:
- 计算机视觉和机器学习是推动植物疾病检测和管理的强大工具.
- 仔细考虑培训数据和模型选择对于成功实施至关重要.
- 本综述提供了这些技术在农业中的实际应用和实施的见解.
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