Image classification on smart agriculture platforms: Systematic literature review

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
Juan Felipe Restrepo-Arias , John W. Branch-Bedoya , Gabriel Awad
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Abstract

In recent years, smart agriculture has gained strength due to the application of industry 4.0 technologies in agriculture. As a result, efforts are increasing in proposing artificial vision applications to solve many problems. However, many of these applications are developed separately. Many academic works have proposed solutions integrating image classification techniques through IoT platforms. For this reason, this paper aims to answer the following research questions: (1) What are the main problems to be solved with smart farming IoT platforms that incorporate images? (2) What are the main strategies for incorporating image classification methods in smart agriculture IoT platforms? and (3) What are the main image acquisition, preprocessing, transmission, and classification technologies used in smart agriculture IoT platforms? This study adopts a Systematic Literature Review (SLR) approach. We searched Scopus, Web of Science, IEEE Xplore, and Springer Link databases from January 2018 to July 2022. From which we could identify five domains corresponding to (1) disease and pest detection, (2) crop growth and health monitoring, (3) irrigation and crop protection management, (4) intrusion detection, and (5) fruits and plant counting. There are three types of strategies to integrate image data into smart agriculture IoT platforms: (1) classification process in the edge, (2) classification process in the cloud, and (3) classification process combined. The main advantage of the first is obtaining data in real-time, and its main disadvantage is the cost of implementation. On the other hand, the main advantage of the second is the ability to process high-resolution images, and its main disadvantage is the need for high-bandwidth connectivity. Finally, the mixed strategy can significantly benefit infrastructure investment, but most works are experimental.

智慧农业平台上的图像分类:系统文献综述
近年来,由于工业 4.0 技术在农业中的应用,智慧农业的发展势头日益强劲。因此,人们越来越努力地提出人工视觉应用来解决许多问题。然而,其中许多应用都是单独开发的。许多学术著作提出了通过物联网平台整合图像分类技术的解决方案。为此,本文旨在回答以下研究问题:(1)结合图像的智能农业物联网平台需要解决哪些主要问题?(2) 将图像分类方法纳入智能农业物联网平台的主要策略是什么? (3) 智能农业物联网平台采用的主要图像采集、预处理、传输和分类技术有哪些?本研究采用了系统文献综述(SLR)方法。我们检索了 2018 年 1 月至 2022 年 7 月期间的 Scopus、Web of Science、IEEE Xplore 和 Springer Link 数据库。从中,我们确定了五个领域,分别是:(1)病虫害检测;(2)作物生长和健康监测;(3)灌溉和作物保护管理;(4)入侵检测;以及(5)水果和植物计数。将图像数据集成到智慧农业物联网平台的策略有三种:(1)边缘分类处理;(2)云端分类处理;(3)分类处理组合。第一种策略的主要优点是实时获取数据,主要缺点是实施成本较高。另一方面,第二种方法的主要优点是能够处理高分辨率图像,其主要缺点是需要高带宽连接。最后,混合策略可大大有利于基础设施投资,但大多数工作都是试验性的。
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
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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