Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

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ID: 266863
2021
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Ranked #118 of 170 articles by views in remote sensing

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Abstract
Crop diseases constitute a serious issue in agriculture, affecting both quality and quantity of agriculture production. Disease control has been a research object in many scientific and technologic domains. Technological advances in sensors, data storage, computing resources and artificial intelligence have shown enormous potential to control diseases effectively. A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc. However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area. This paper reviews state-of-the-art machine learning methods that use different data sources, applied to plant disease detection. It lists traditional and deep learning methods associated with the main data acquisition modalities, namely IoT, ground imaging, unmanned aerial vehicle imaging and satellite imaging. In addition, this study examines the role of data fusion for ongoing research in the context of disease detection. It highlights the advantage of intelligent data fusion techniques, from heterogeneous data sources, to improve plant health status prediction and presents the main challenges facing this field. The study concludes with a discussion of several current issues and research trends.
Reference Key
ouhami2021remotecomputer Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Maryam Ouhami;Adel Hafiane;Youssef Es-Saady;Mohamed El Hajji;Raphael Canals;Ouhami, Maryam;Hafiane, Adel;Es-Saady, Youssef;El Hajji, Mohamed;Canals, Raphael;
Journal remote sensing
Year 2021
DOI
10.3390/rs13132486
URL
https://doi.org/10.3390/rs13132486
Keywords
Machine learning remote sensing data fusion plant disease intelligent sensors

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