Crop mapping using supervised machine learning and deep learning: a systematic literature review

The ever-increasing global population presents a looming threat to food production. To meet growing food demands while minimizing negative impacts on water and soil, agricultural practices must be altered. To make informed decisions, decision-makers require timely, accurate, and efficient crop maps....

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Bibliographic Details
Published inInternational journal of remote sensing Vol. 44; no. 8; pp. 2717 - 2753
Main Authors Alami Machichi, Mouad, mansouri, loubna El, imani, yasmina, Bourja, Omar, Lahlou, Ouiam, Zennayi, Yahya, Bourzeix, François, Hanadé Houmma, Ismaguil, Hadria, Rachid
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 18.04.2023
Taylor & Francis Ltd
Subjects
Agricultural practices
Algorithms
Big Data
Crop mapping
Crop rotation
Decision making
Deep learning
Double cropping
Food
Food production
Foods
Lidar
Literature reviews
Machine learning
Mapping
Remote sensing
Remote sensing techniques
Remote sensors
Sensing techniques
Soil water
Supervised learning
Systematic review
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Summary:The ever-increasing global population presents a looming threat to food production. To meet growing food demands while minimizing negative impacts on water and soil, agricultural practices must be altered. To make informed decisions, decision-makers require timely, accurate, and efficient crop maps. Remote sensing-based crop mapping faces numerous challenges. However, recent years have seen substantial advances in crop mapping through the use of big data, multi-sensor imagery, the democratization of remote sensing data, and the success of deep learning algorithms. This systematic literature review provides an overview of the history and evolution of crop mapping using remote sensing techniques. It also discusses the latest scientific advances in the field of crop mapping, which involve the use of machine and deep learning models. The review protocol involved the analysis of 386 peer-reviewed publications. The results of the analysis show that areas such as crop rotation mapping, double cropping, and early crop mapping require further exploration. The use of LiDAR as a tool for crop mapping also needs more attention, and hierarchical crop mapping is recommended. This review provides a comprehensive framework for future researchers interested in accurate large-scale crop mapping from multi-source image data and machine and deep learning techniques.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2023.2205984