作物产量和病害检测中的数据科学方法微型综述

IF 3.5 Q1 AGRONOMY
Lorenzo Valleggi, Federico Mattia Stefanini
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引用次数: 0

摘要

农业是一个对环境影响相当大的部门,随着人口的预计增长,这一问题也将日益严重,从而扩大对公共健康的影响。要有效减轻和管理这种影响,就必须采用智能技术和数据驱动方法,这些技术和方法统称为精准农业。虽然某些方法得到了广泛认可,但其他方法尽管不那么显眼,也做出了有意义的贡献。本微型综述报告讨论了过去五年中精准农业领域流行的人工智能技术,特别强调了当前文献中广泛研究的作物产量预测和疾病检测领域。主要目的是全面概述人工智能在农业中的应用,包括机器学习、深度学习和统计方法。这种方法旨在弥补一个明显的不足,即现有的综述主要侧重于单一方面,而不是提出一个统一和包容的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A mini-review on data science approaches in crop yield and disease detection
Agriculture constitutes a sector with a considerable environmental impact, a concern that is poised to increase with the projected growth in population, thereby amplifying implications for public health. Effectively mitigating and managing this impact demands the implementation of intelligent technologies and data-driven methodologies collectively called precision agriculture. While certain methodologies enjoy widespread acknowledgement, others, despite their lesser prominence, contribute meaningfully. This mini-review report discusses the prevalent AI technologies within precision agriculture over the preceding five years, with a specific emphasis on crop yield prediction and disease detection domains extensively studied within the current literature. The primary objective is to give a comprehensive overview of AI applications in agriculture, spanning machine learning, deep learning, and statistical methods. This approach aims to address a notable gap wherein existing reviews predominantly focus on singular aspects rather than presenting a unified and inclusive perspective.
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来源期刊
Frontiers in Agronomy
Frontiers in Agronomy Agricultural and Biological Sciences-Agricultural and Biological Sciences (miscellaneous)
CiteScore
4.80
自引率
0.00%
发文量
123
审稿时长
13 weeks
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