How can aerial imagery and vegetation indices algorithms monitor the geotagged crop?

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Vikneswaran Jeya Kumaran , Nur Adibah Mohidem , Nik Norasma Che’Ya , Wan Fazilah Fazlil Ilahi , Jasmin Arif Shah , Zulhilmy Sahwee , Norhakim Yusof , Mohammad Husni Omar
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Abstract

There is very little to no literature on the use of geotagging to monitor crops from aerial photos, even though many technologies have been created to do so. Current crop monitoring methods, relying on field data and lab analysis, are inefficient due to high labor, time, and potential harm, limiting their broad use. With the use of vegetation indices (VI) and geotagging, this paper highlights the benefits of crop-specific monitoring with unmanned aerial vehicles (UAV). This study systematically searched the original articles published from the 1st of January 2016 to the 7th of October 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “aerial imagery” AND “vegetation index” OR “vegetation indices“ AND “crop”. Out of the papers identified, 28 eligible studies did meet our inclusion criteria and were evaluated. This review thoroughly discusses the advantages of aerial imagery, vegetation indices, and geotagging tools in the context of crop monitoring. It was found that geotagged crop monitoring using UAV empowers farmers with data-driven insights using vegetation indices, enabling them to make informed decisions before acting, transforming agriculture towards a digital future. This study offers valuable insights for researchers and industry players, helping them identify effective and context-specific crop monitoring strategies for diverse plantations, crops, and budgets. Moreover, by utilizing the advanced computational capabilities of artificial intelligence (AI), we can analyze a wide range of vegetation indices to gain a comprehensive understanding of crop health and conduct accurate predictions.

航空图像和植被指数算法如何监测地理标记作物?
尽管已经有很多技术可以利用航拍照片监测作物,但关于利用地理标记监测作物的文献却少之又少。目前的农作物监测方法依赖于实地数据和实验室分析,由于耗费大量人力、时间和潜在危害,效率低下,限制了其广泛应用。通过使用植被指数(VI)和地理标记,本文强调了利用无人飞行器(UAV)进行特定作物监测的好处。本研究使用布尔字符串系统地检索了 Scopus、ScienceDirect、Commonwealth Agricultural Bureaux (CAB) Direct 和 Web of Science (WoS) 数据库中 2016 年 1 月 1 日至 2021 年 10 月 7 日发表的原始文章:"航空图像 "和 "植被指数 "或 "植被指数 "和 "作物"。在确定的论文中,有 28 项符合纳入标准的研究接受了评估。本综述深入探讨了航空图像、植被指数和地理标记工具在作物监测方面的优势。研究发现,利用无人机对作物进行地理标记监测,能让农民利用植被指数获得数据驱动的洞察力,使他们在行动之前就能做出明智的决策,从而将农业转变为数字化的未来。这项研究为研究人员和业内人士提供了宝贵的见解,帮助他们针对不同的种植园、作物和预算,确定有效且符合具体情况的作物监测策略。此外,通过利用人工智能(AI)的先进计算能力,我们可以分析各种植被指数,从而全面了解作物健康状况并进行准确预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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