Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

•Review of studies in crop yield prediction and N status estimation via ML techniques.•Comparison of different ML techniques in application to the same task in PA.•Discussion on ML techniques used in the reviewed studies. Accurate yield estimation and optimised nitrogen management is essential in ag...

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Bibliographic Details
Published in:Computers and electronics in agriculture 2018-08, Vol.151, p.61-69
Main Authors: Chlingaryan, Anna, Sukkarieh, Salah, Whelan, Brett
Format: Article
Language:English
Subjects:
Agricultural practices
Agricultural production
Agriculture
Artificial intelligence
Crop yield
Decision making
Environmental impact
Features extraction
Hybrid systems
Information fusion
Machine learning
Nitrogen
Operating costs
Platforms
Predictive modelling
Remote sensing
Signal processing
State estimation
Vegetation indices
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Summary:•Review of studies in crop yield prediction and N status estimation via ML techniques.•Comparison of different ML techniques in application to the same task in PA.•Discussion on ML techniques used in the reviewed studies. Accurate yield estimation and optimised nitrogen management is essential in agriculture. Remote sensing (RS) systems are being more widely used in building decision support tools for contemporary farming systems to improve yield production and nitrogen management while reducing operating costs and environmental impact. However, RS based approaches require processing of enormous amounts of remotely sensed data from different platforms and, therefore, greater attention is currently being devoted to machine learning (ML) methods. This is due to the capability of machine learning based systems to process a large number of inputs and handle non-linear tasks. This paper discusses research developments conducted within the last 15 years on machine learning based techniques for accurate crop yield prediction and nitrogen status estimation. The paper concludes that the rapid advances in sensing technologies and ML techniques will provide cost-effective and comprehensive solutions for better crop and environment state estimation and decision making. More targeted application of the sensor platforms and ML techniques, the fusion of different sensor modalities and expert knowledge, and the development of hybrid systems combining different ML and signal processing techniques are all likely to be part of precision agriculture (PA) in the near future.
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2018.05.012