用人工智能节省作物:植物病理学深度学习技术的最新趋势
Zafar Salman1, Abdullah Muhammad1, Md Jalil Piran1
1Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea.
Frontiers in plant science
|August 21, 2023
概括
本次调查回顾了用于检测植物疾病的机器学习,强调了精准农业的进步和挑战. 它提供了对数据集,技术和改善作物健康管理的未来趋势的见解.
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:
- 植物病严重威胁全球粮食安全和农业生产力.
- 深度学习 (DL) 为精准农业中疾病检测提供了潜在的解决方案.
- 现有的DL方法用于植物疾病识别既有优势,也有局限性.
研究的目的:
- 提供基于机器学习的植物疾病检测方法的全面概述.
- 分析该领域普遍存在的数据集,技术和新兴方法.
- 识别提高作物健康管理的挑战和潜在改进.
主要方法:
- 对用于检测植物疾病的机器学习的最新进展进行系统审查.
- 分析常用的数据集和深度学习技术.
- 探索新兴战略及其相关挑战.
主要成果:
- 在应用深度学习来检测植物疾病方面取得了重大进展.
- 强调了使用的数据集和方法的多样性.
- 记录了实施中的挑战和未来研究领域.
结论:
- 机器学习,特别是深度学习,对精准农业和作物健康有很大的前景.
- 需要进一步的研究来克服实施挑战和优化现有技术.
- 这项调查是研究人员和从业人员的宝贵资源,旨在推进作物疾病管理.
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