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

By A. Chlingaryan, Salah Sukkarieh, B. Whelan. Computers and Electronics in Agriculture, 2018

1,268 citations recorded by the source corpus.

Abstract 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.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/78f232ab1d6001acc6c332b75777c0d531d0df0b

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Citations in the corpus

A systematic literature review on machine learning applications for sustainable agriculture supply chain performance (2020, background): “The literature in the production phase indicated that the site-specific and optimized nutrient management using ML algorithms ensure accurate crop yield and productivity (Chlingaryan et al. 2018).”
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, background): “Machine learning or deep learning is capable of processing multi‐source and multi‐type data [202].”
Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “Last but not least, the development of efficient ML techniques by incorporating expert knowledge from different stakeholders should be fostered, particularly regarding computing science, agriculture, and the private sector, as a means of designing realistic solutions [19,22,24,33].”
Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “Agricultural yield forecasts over large areas can help policymakers and grain marketing agencies plan for exports and imports (Chlingaryan et al., 2018; Johnson et al., 2016).”
Machine Learning Applications for Precision Agriculture: A Comprehensive Review (2021, methodology): “[30] discussed different ML approaches used in precision agriculture for accurate crop yield prediction and soil nitrogen estimation over the last fifteen years.”
Machine learning and soil sciences: a review aided by machine learning tools (2019, background): “While we can narrow down a narrative of ML application in a specific research question, such as the application of ML in yield prediction in precision agriculture (Chlingaryan et al., 2018) or DSM, it is an impossible task to manually review all papers on the application of ML in soil science.”
Machine learning for large-scale crop yield forecasting (2020, background): “Using these indicators, we designed 3 types of features: (i) maximum D. Paudel et al. values for accumulative indicators, such water-limited yield biomass, (ii) counts of days or dekads for indicators related to extreme conditions, such as maximum temperature, and (iii) average values for other indi”