Data analytics for crop management: a big data view

Journal of Big Data

Survey

Nabila Chergui¹  Mohand Tahar Kechadi² 

[1] Faculty of Technology, Ferhat Abbas University, Sétif, Algeria;Faculty of Technology, Ferhat Abbas University, Sétif, Algeria;School of Computer Science, University College Dublin, Dublin, Ireland;

关键词: Digital agriculture;    Data analytics;    Crop management;    Big data;    Data mining;    Machine learning;   

DOI  :  10.1186/s40537-022-00668-2

 received in 2022-01-10, accepted in 2022-11-29,  发布年份 2022

来源: Springer

【 摘 要 】

Recent advances in Information and Communication Technologies have a significant impact on all sectors of the economy worldwide. Digital Agriculture appeared as a consequence of the democratisation of digital devices and advances in artificial intelligence and data science. Digital agriculture created new processes for making farming more productive and efficient while respecting the environment. Recent and sophisticated digital devices and data science allowed the collection and analysis of vast amounts of agricultural datasets to help farmers, agronomists, and professionals understand better farming tasks and make better decisions. In this paper, we present a systematic review of the application of data mining techniques to digital agriculture. We introduce the crop yield management process and its components while limiting this study to crop yield and monitoring. After identifying the main categories of data mining techniques for crop yield monitoring, we discuss a panoply of existing works on the use of data analytics. This is followed by a general analysis and discussion on the impact of big data on agriculture.

【 授权许可】

CC BY   
© The Author(s) 2022

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