Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors

A.A. Masrur Ahmed, E. Sharma, S. Janifer Jabin Jui, R.C. Deo, T. Nguyen-Huy, M. Ali

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This paper incorporates remote satellite-based information in a wheat-growing region in South Australia to estimate the yield by integrating the kernel ridge regression (KRR) method coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the grey wolf optimisation (GWO). The hybrid model, ‘GWO-CEEMDAN-KRR,’ employing an initial pool of 23 different satellite-based predictors, is seen to outperform all the benchmark models and all the feature selection (ant colony, atom search, and particle swarm optimisation) methods that are implemented using a set of carefully screened satellite variables and a feature decomposition or CEEMDAN approach. A suite of statistical metrics and infographics comparing the pre-dicted and measured yield shows a model prediction error that can be reduced by ~20% by employing the proposed GWO-CEEMDAN-KRR model. With the metrics verifying the accuracy of simulations, we also show that it is possible to optimise the wheat yield to achieve agricultural profits by quantifying and including the effects of satellite variables on potential yield. With further improvements in the proposed methodology, the GWO-CEEMDAN-KRR model can be adopted in agricultural yield simulation that requires remote sensing data to establish the relationships between crop health, yield, and other productivity features to support precision agriculture. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Original languageEnglish
JournalRemote Sensing
Volume14
Issue number5
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • Kernel ridge regression
  • Machine learning
  • Satellite data
  • South Australia
  • Wheat yield
  • Agricultural machinery
  • Ant colony optimization
  • Commerce
  • Food supply
  • Forecasting
  • Grain (agricultural product)
  • Particle swarm optimization (PSO)
  • Regression analysis
  • Remote sensing
  • Adaptive noise
  • Empirical Mode Decomposition
  • Gray wolves
  • Kernel ridge regressions
  • Optimisations
  • Regression modelling
  • Yield prediction
  • Satellites

Fingerprint

Dive into the research topics of 'Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors'. Together they form a unique fingerprint.

Cite this