Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review

IF 3.3 2区 农林科学 Q1 AGRONOMY Agronomy-Basel Pub Date : 2023-08-31 DOI:10.3390/agronomy13092302
Shweta Pokhariyal, N. R. Patel, A. Govind
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Abstract

In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing “crop management”, “soil management”, and “water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management.
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机器学习驱动的遥感在印度农业中的应用——系统综述
在印度,农业是经济的支柱,也是就业的主要来源。尽管2019冠状病毒病大流行造成了挫折,但印度的农业和相关部门表现出了韧性,在2020 - 2021年期间实现了3.4%的增长,尽管同期整体经济增长下降了7.2%。改善农业部门对于维持不断增长的人口和保障粮食安全至关重要。因此,世界各地的研究人员一直致力于利用先进技术建立智能、可持续和有利可图的农业系统,从而实现农业的数字化转型。遥感(RS)和机器学习(ML)的进步已被证明有利于农民和决策者通过有价值的作物见解来最大限度地减少作物损失和优化资源利用。在本文中,我们对致力于RS和ML在解决印度农业相关挑战中的应用的研究进行了全面回顾。我们按照系统评价和荟萃分析(PRISMA)指南的首选报告项目进行了系统文献综述,并评估了2015年至2022年发表的研究文章。本研究的目的是阐明RS和ML技术在关键农业领域的应用,包括“作物管理”、“土壤管理”和“水管理”,最终导致它们的改进。本研究主要侧重于评估印度农业中使用智能地理空间数据分析的现状。大多数研究是在作物管理类别中进行的,在这一类别中,各种遥感传感器的部署导致农业监测方面的重大改进。遥感技术和机器学习技术的集成可以实现农业监测的智能方法,从而为有效的农业管理提供有价值的建议和见解。
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来源期刊
Agronomy-Basel
Agronomy-Basel Agricultural and Biological Sciences-Agronomy and Crop Science
CiteScore
6.20
自引率
13.50%
发文量
2665
审稿时长
20.32 days
期刊介绍: Agronomy (ISSN 2073-4395) is an international and cross-disciplinary scholarly journal on agronomy and agroecology. It publishes reviews, regular research papers, communications and short notes, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles.
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