葡萄树研究中的人工智能技术:一项比较研究,对数据集,疾病和技术进行了广泛的审查
Paraskevi Gatou1, Xanthi Tsiara1, Alexandros Spitalas1
1Computer Engineering and Informatics Department, University of Patras, Panepistimioupoli, 26504 Rio, Achaia, Greece.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
机器学习 (ML) 正在彻底改变农业,特别是葡萄种植,用于检测和预测葡萄疾病. 卷积神经网络在确定可持续农业的感染方面显示出有希望的结果.
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:
- 农业部门越来越多地采用数字化转型和人工智能 (AI).
- 机器学习 (ML) 为精准农业的数据驱动挑战提供了强大的解决方案.
- 葡萄种植面临着葡萄感染带来的重大挑战,影响产量和质量.
研究的目的:
- 对农业中的ML应用进行综合性文献审查 (2017-2023年),重点关注葡萄种植.
- 识别和分析用于检测和预测葡萄感染的ML算法.
- 为未来的研究编制可用的葡萄藤数据集.
主要方法:
- 2017年至2023年科学文献的系统审查.
- 分析机器学习算法,重点是神经网络和卷积神经网络 (CNN).
- 编制和描述公开可用的葡萄酒数据集 (统计,基于图像).
主要成果:
- 88%的审查研究是在过去五年发表的,这表明快速增长.
- 神经网络,特别是CNN,经常为疾病检测带来最好的结果.
- 研究的主要疾病包括葡萄藤黄色,黄,埃斯卡,菌,叶子,皮尔斯病和根腐.
结论:
- ML,特别是CNN,显示出早期检测和预测葡萄藤疾病的巨大潜力.
- 这篇评论为人工智能研究人员,农业科学家和葡萄种植者提供了宝贵的资源.
- 通过ML有效的疾病管理可以提高葡萄种植和酒的可持续性和弹性.
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