Machine Learning in Agriculture: A Comprehensive Updated Review.
Machine Learning in Agriculture: A Comprehensive Updated Review.
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DOI：
10.3390/s21113758
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发表时间：
2021-05-28
期刊：
影响因子：
--
通讯作者：
Bochtis D
中科院分区：
文献类型：
--
作者：
Benos L;Tagarakis AC;Dolias G;Berruto R;Kateris D;Bochtis D
农业数字化转型将管理的各个方面演变为人工智能系统，以便从众多来源的不断增加的数据中创造价值。人工智能的一个子集，即机器学习，具有巨大的潜力来应对建立基于知识的农业系统中的众多挑战。本研究旨在根据“机器学习”与“作物管理”、“水管理”、“土壤管理”和“牲畜管理”的关键词组合，并根据 PRISMA 指南，深入回顾最近的学术文献，以阐明农业中的机器学习。只有 2018 年至 2020 年期间发表的期刊论文才被视为符合资格。结果表明，该主题属于不同学科，有利于国际层面的融合研究。此外，作物管理也成为人们关注的焦点。使用了大量的机器学习算法，其中属于人工神经网络的算法效率更高。此外，玉米和小麦以及牛和羊分别是调查最多的农作物和动物。最后，安装在卫星以及无人驾驶地面和飞行器上的各种传感器已被用作获取用于数据分析的可靠输入数据的手段。预计这项研究将为所有利益相关者提供有益的指导，以提高对农业中使用机器学习的潜在优势的认识，并有助于对该主题进行更系统的研究。
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.
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影响因子：
8.3
作者：
Akbarzadeh, Saman;Paap, Arie;Alameh, Kamal
通讯作者：
Alameh, Kamal
影响因子：
8.3
作者：
Abdulridha, Jaafar;Ehsani, Reza;Ampatzidis, Yiannis
通讯作者：
Ampatzidis, Yiannis
影响因子：
2.7
作者：
Afzaal, Hassan;Farooque, Aitazaz A.;Esau, Travis
通讯作者：
Esau, Travis
影响因子：
2.5
作者：
Ahmad, Ishfaq;Saeed, Umer;Judge, Jasmeet
通讯作者：
Judge, Jasmeet
DOI：
10.3390/s18103408
发表时间：
2018-10-11
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Adeyemi O;Grove I;Peets S;Domun Y;Norton T
通讯作者：
Norton T