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An Approach for Prediction of Weekly Prices of Green Chili in Sri Lanka: Application of Artificial Neural Network Techniques

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dc.contributor.author Basnayake, B.R.P.M.
dc.contributor.author Kaushalya, K.D.
dc.contributor.author Wickaramarathne, R.H.M.
dc.contributor.author Kushan, M.A.K.
dc.contributor.author Chandrasekara, N.V.
dc.date.accessioned 2022-08-12T06:51:16Z
dc.date.available 2022-08-12T06:51:16Z
dc.date.issued 2022
dc.identifier.citation Basnayake, B.R.P.M., Kaushalya, K.D., Wickaramarathne, R.H.M., Kushan, M.A.K. and Chandrasekara, N.V.C., 2022. An Approach for Prediction of Weekly Prices of Green Chili in Sri Lanka: Application of Artificial Neural Network Techniques. Journal of Agricultural Sciences – Sri Lanka, 17(2), pp.333–349. DOI: http://doi.org/10.4038/jas.v17i2.9746 en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/25062
dc.description.abstract Purpose: Predicting the prices of crops is a principal task for producers, suppliers, governments and international businesses. The purpose of the study is to forecast the prices of green chili, which is a cash crop in Sri Lanka. Artificial neural networks were applied as they help to extract important insights from the bulk of data with a scientific approach. Research Method: The Time Delay Neural Network (TDNN), Feedforward Neural Network (FFNN) with Levenberg-Marquardt (LM) algorithm and FFNN with Scaled Conjugate Gradient (SCG) algorithm were employed on weekly average retail prices of green chili in Sri Lanka from the 1st week of January 2011 to the 4th week of December 2018. The performance of models was evaluated through the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Normalized Mean Squared Error (NMSE). Findings: Among the three methods implemented, the FFNN model using the LM algorithm exhibited the highest accuracy with a minimum MSE of 0.0033, MAE of 0.0437 and NMSE of 0.2542. The model built using the SCG algorithm fitted data with a minimum MSE of 0.0033, MAE of 0.0458 and NMSE of 0.2549. Among the fitted TDNN models, the model with 8 input delays were a better model with an MSE of 0.0036, MAE of 0.0470 and NMSE of 0.3221. FFNNs outperformed TDNN in forecasting green chili prices of Sri Lanka. Originality/ Value: The neural network approach in forecasting the prices of green chili provides more accurate results to make decisions based on the trends and to identify future opportunities. en_US
dc.publisher Journal of Agricultural Sciences – Sri Lanka en_US
dc.subject Green Chili, Feedforward Neural Network, Levenberg-Marquardt algorithm, Prediction, Scaled Conjugate Gradient algorithm, Time Delay Neural Network en_US
dc.title An Approach for Prediction of Weekly Prices of Green Chili in Sri Lanka: Application of Artificial Neural Network Techniques en_US


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