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Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

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  • Vinushi Amaratunga
  • Lasini Wickramasinghe
  • Anushka Perera
  • Jeevani Jayasinghe
  • Upaka Rathnayake

Abstract

Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient ( R ) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.

Suggested Citation

  • Vinushi Amaratunga & Lasini Wickramasinghe & Anushka Perera & Jeevani Jayasinghe & Upaka Rathnayake, 2020. "Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, July.
  • Handle: RePEc:hin:jnlmpe:8627824
    DOI: 10.1155/2020/8627824
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    Cited by:

    1. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    2. Dominika Sieracka & Maciej Zaborowicz & Jakub Frankowski, 2023. "Identification of Characteristic Parameters in Seed Yielding of Selected Varieties of Industrial Hemp ( Cannabis sativa L.) Using Artificial Intelligence Methods," Agriculture, MDPI, vol. 13(5), pages 1-11, May.
    3. Priya Brata Bhoi & Veeresh S. Wali & Deepak Kumar Swain & Kalpana Sharma & Akash Kumar Bhoi & Manlio Bacco & Paolo Barsocchi, 2021. "Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach," Agriculture, MDPI, vol. 11(9), pages 1-27, August.
    4. Pradyot Ranjan Jena & Babita Majhi & Rajesh Kalli & Ritanjali Majhi, 2023. "Prediction of crop yield using climate variables in the south-western province of India: a functional artificial neural network modeling (FLANN) approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 11033-11056, October.
    5. Mahdieh Parsaeian & Mohammad Rahimi & Abbas Rohani & Shaneka S. Lawson, 2022. "Towards the Modeling and Prediction of the Yield of Oilseed Crops: A Multi-Machine Learning Approach," Agriculture, MDPI, vol. 12(10), pages 1-23, October.
    6. Christopher K. Wikle & Abhirup Datta & Bhava Vyasa Hari & Edward L. Boone & Indranil Sahoo & Indulekha Kavila & Stefano Castruccio & Susan J. Simmons & Wesley S. Burr & Won Chang, 2023. "An illustration of model agnostic explainability methods applied to environmental data," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    7. Shahram Rezapour & Erfan Jooyandeh & Mohsen Ramezanzade & Ali Mostafaeipour & Mehdi Jahangiri & Alibek Issakhov & Shahariar Chowdhury & Kuaanan Techato, 2021. "Forecasting Rainfed Agricultural Production in Arid and Semi-Arid Lands Using Learning Machine Methods: A Case Study," Sustainability, MDPI, vol. 13(9), pages 1-28, April.

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