Estimate soil moisture of maize by combining support vector machine and chaotic whale optimization algorithm

Bohao He, Biying Jia, Yanghe Zhao, Xu Wang, Mao Wei and Ranae Dietzel

Agricultural Water Management, 2022, vol. 267, issue C

Abstract: Soil moisture of maize has an extremely important impact on the growth and development of maize. Failure to accurately estimate soil moisture will lead to severe reductions in maize yields and thus intensify the global food crisis, so it is extremely important to accurately estimate soil moisture of maize. This study proposes a new hybrid machine learning model (SVM-SWOA) that incorporates the Whale Optimization Algorithm (WOA) into sinusoidal chaotic graphs and couples it with a support vector machine (SVM). The model is with both high convergence speed and high accuracy. After using the data from two maize agricultural districts in Iowa, USA for model creation, Taylor plots and significance tests were used to enable the model for identifying input variables. To verify the performance of the model, SVM-SWOA was comprehensively evaluated with both SVM and SVM-WOA models. Results showed that SVM-SWOA was improved 14%, 13%, 41.5%, and 14% over SVM-WOA at 60 cm depth for MAE, RMSE, MAPE, and MBE, respectively, and 20%, 29.5%, 44.5%, and 38% over SVM, respectively. It implies that the SVM-SWOA meta-heuristic algorithm can provide better guidance for smart agriculture and precision irrigation.

Keywords: Soil moisture; Support vector machine; Hybrid model; Agricultural irrigation; Time series forecasting; Smart agriculture (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377422001652
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:267:y:2022:i:c:s0378377422001652

DOI: 10.1016/j.agwat.2022.107618

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().