Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction
Article
Article Title | Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction |
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ERA Journal ID | 201487 |
Article Category | Article |
Authors | Ali, Mumtaz (Author), Deo, Ravinesh C. (Author), Xiang, Yong (Author), Prasad, Ramendra (Author), Li, Jianxin (Author), Farooque, Aitazaz (Author) and Yaseen, Zaher Mundher (Author) |
Journal Title | Scientific Reports |
Journal Citation | 12 (1), pp. 1-23 |
Article Number | 5488 |
Number of Pages | 23 |
Year | 2022 |
Publisher | Nature Publishing Group |
Place of Publication | United Kingdom |
ISSN | 2045-2322 |
Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-022-09482-5 |
Web Address (URL) | https://www.nature.com/articles/s41598-022-09482-5 |
Abstract | Inadequate agricultural planning compounded by inaccurate predictions results in an inflated local market rate and prompts higher importation of wheat. To tackle this problem, this research has designed two-phase universal machine learning (ML) model to predict wheat yield (Wpred), utilizing 27 agricultural counties’ data within the Agro-ecological zone. The universal model, online sequential extreme learning machines coupled with ant colony optimization (ACO-OSELM) is developed, by incorporating the significant annual yield data lagged at (t − 1) as the model’s predictor to generate future yield at 6 test stations. In the first phase, ACO is adopted to search for suitable, statistically relevant data stations for model training, and the corresponding test station by virtue of a feature selection strategy. An annual wheat yield time-series input dataset is constructed utilizing data from each selected training station (1981–2013) and applied against 6 test stations (with each case modelled with 26 station data as the input) to evaluate the hybrid ACO-OSELM model. The partial autocorrelation function is implemented to deduce statistically significant lagged data, and OSELM is applied to generate Wpred. The two-phase hybrid ACO-OSELM model is tested within the 6 agricultural districts (represented as stations) of Punjab province, Pakistan and the results are benchmarked with extreme learning machine (ELM) and random forest (RF) integrated with ACO (i.e., hybrid ACO-ELM and hybrid ACO-RF models, respectively). The performance of the ACO-OSELM model was proven to be good in comparison to ACO-ELM and ACO-RF models. The hybrid ACO-OSELM model revealed its potential to be implemented as a decision-making system for crop yield prediction in areas where a significant association with the historical agricultural crop is well-established. |
Keywords | Algorithms; Crops, Agricultural; Education, Distance; Machine Learning; Triticum |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
300299. Agriculture, land and farm management not elsewhere classified | |
Byline Affiliations | Deakin University |
International Centre for Applied Climate Science | |
University of Fiji, Fiji | |
University of Prince Edward Island, Canada | |
School of Mathematics, Physics and Computing | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q759w/coupled-online-sequential-extreme-learning-machine-model-with-ant-colony-optimization-algorithm-for-wheat-yield-prediction
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