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用人工智能彻底改变农业:植物疾病检测方法,应用及其局限性
Abbas Jafar1, Nabila Bibi2, Rizwan Ali Naqvi3
1HPC Laboratory, Department of Computer Engineering, Myongji University, Yongin, Republic of Korea.
Frontiers in plant science
|March 28, 2024
概括
使用人工智能 (AI) 和物联网 (IoT) 的自动植物疾病检测为传统方法提供了更快,更准确的替代方案. 这项研究审查了人工智能驱动的方法来诊断番茄,,马和黄瓜作物的疾病,从而提高农业产量.
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:
- 准确的植物病检测对于农业生产力和防止经济损失至关重要.
- 传统的方法是缓慢的,需要专业知识,并且资源密集.
- 病毒,真菌和细菌等传染性生物对作物生产构成重大挑战.
研究的目的:
- 通过人工智能 (AI) 审查和概述预先确定的步骤来预测植物疾病.
- 检查番茄,,马和黄瓜作物中普遍存在的疾病和症状.
- 讨论用于自动检测疾病的机器学习 (ML) 和深度学习 (DL) 模型.
主要方法:
- 图像采集,预处理,细分,特征选择和分类是人工智能的关键步骤.
- 审查现有的ML和DL研究,以检测四种特定作物的疾病.
- 用于基于ML和DL检测植物疾病的数据集的分析.
主要成果:
- 人工智能和物联网传感器方法为植物疾病分析提供了自动化解决方案.
- 在番茄,,土豆和黄瓜中确定了流行疾病和症状.
- 编制了植物疾病检测数据集列表,以便进一步研究.
结论:
- 人工智能,特别是ML和DL,比传统的植物疾病检测方法提供了显著的改进.
- 未来的研究应该专注于将AI与物联网平台集成,例如用于实时现场监控的智能无人机.
- 本综述有助于从业人员了解当前的植物疾病检测策略和局限性.
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