Lasini Wickramasinghe

Work place: Department of Electronics, Faculty of Applied Sciences, Wayamba University of Sri Lanka, Kuliyapitiya, 60200, Sri Lanka



Research Interests: Machine Learning


Lasini Wickramasinghe received her B.Sc. (Joint Major) degree from Wayamba University of Sri Lanka, Kuliyapitiya, Sri Lanka in 2020 and reading for MPhil in electrical and electronic engineering at Faculty of Engineering, University of Peradeniya, Sri Lanka.

She is currently working as a Demonstrator at Wayamba University of Sri Lanka. Her research areas include machine learning based modelling and statistical data analysis. She has published more than 10 research articles/abstracts in journals and conference proceedings.

Author Articles
Development of Crop-Weather Models Using Gaussian Process Regression for the Prediction of Paddy Yield in Sri Lanka

By Piyal Ekanayake Lasini Wickramasinghe Jeevani W. Jayasinghe

DOI:, Pub. Date: 8 Aug. 2022

This research introduces machine learning models using the Gaussian Process Regression (GPR) depicting the association between paddy yield and weather in Sri Lanka. All major regions in the island with most contribution to the total paddy production were considered in this research. The climatic factors of rainfall, relative humidity, minimum temperature, maximum temperature, average wind speed, evaporation, and sunshine hours were considered as input (independent) variables, while the paddy yield was the output (dependent) variable. The collinearity within each pair of independent and dependent variables was determined using Spearman’s and Pearson’s correlation coefficients. Data sets corresponding to the two main annual paddy cultivation seasons since 2009 were trained in MATLAB to develop crop-weather models. The most appropriate Kernel function was chosen from among four types of Kernels viz. Rational Quadratic, Exponential, Squared Exponential, and Matern 5/2 based on their degree of coherence in modeling. This approach exploits the full potential of GPR in developing highly accurate crop-weather models. The performance of the crop-weather models was measured by the Correlation Coefficient, Mean Absolute Percentage Error, Mean Squared Error, Root Mean Squared Error Ratio, Nash Number and the BIAS. All the GPR-based models proposed in this paper are highly accurate in terms of the aforementioned evaluation metrics. Accordingly, when the climatic data are known or projected, the paddy yield and thereby the harvest of Sri Lanka can be predicted precisely by using the proposed crop-weather models.

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