Jeevani W. Jayasinghe

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



Research Interests:


Jeevani W. Jayasinghe received Ph.D. in Engineering and B.Sc. (Hons.) in Engineering from the University of Peradeniya, Sri Lanka in 2014 and 2006 respectively. She was a Visiting Fellow of Macquarie University, Sydney, Australia in 2018 and a Research fellow of Ramon Llull University, Spain in 2013.

She is working as a Professor of the Department of Electronics, Wayamba University of Sri Lanka. She has published more than 80 research articles in journals and conference proceedings.

Prof. Jayasinghe is a Senior Member of IEEE, and an Associate Member of Institute of Engineers Sri Lanka. Her current Google scholar h-index is 13 and Scopus h-index is 11.

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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