Monitoring Bambara Groundnut Canopy State Variables at Various Growth Stages Using Low-Cost Remote Sensing Technology and Machine Learning Techniques

Abstract:

The aim of this study was to assess the efficacy of an unmanned aerial vehicle-based remote sensing system for quantifying Bambara groundnut canopy state variables. Remotely sensed color infrared images and in-situ canopy state variables were collected during Malaysia's 2018/19 Bambara growing season at vegetative, flowering, podding, podfilling, maturity, and senescence stages. Five common vegetation indices (VIs) were derived from the images, yielding to single stage and cumulative VIs (∑VIs). The relationship between canopy state variables and single stage VIs/∑VIs was investigated using Pearson’s correlation. Linear parametric and non-linear non-parametric machine learning (ML) regressions were employed to estimate canopy state variables by using VIs/ ∑VIs as input features. The best correlation were observed at flowering stage. The ∑VIs from vegetative to senescence stage exhibited the most robust relationship with canopy state variables. CatBoostRegressor (CBR) excelled in training for all canopy state variables, however, it showed potential overfitting in testing. In contrast, Huber regression (HR) models provided consistent results in both training and testing. HR performance was comparable to that of the top-performing ML algorithms in estimation of groundnut crop variables.

• Periodical: Advances in Science and Technology (Volume 144)
• Pages: 63-69
• DOI: https://doi.org/10.4028/p-BU8h1f
• Online since: March 2024
• Authors: Shaikh Yassir Yousouf Jewan*, Lawal Billa, Debbie Sparkes, Erik Murchie, Vinay Pagay, Deepak Gautam, Alessia Cogato, Ajit Singh
• Keywords: Canopy State Variables, Color Infrared Camera, Drone, Machine Learning, Modelling

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