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

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.

Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka

“…ough paddy yield prediction models were developed by applying numerous techniques [35,36], this is the first research study on developing a crop-weather model for the paddy yield in Sri Lanka. is research can be extended for the prediction of paddy yield for future seasons or years if the independent variables are available as projected climatic variables.…”

Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

“…is model was validated with an accuracy of 97.5%, a sensitivity of 96.3%, and specificity of 98.1% by developing a multilayer perceptron neural network. A similar research work performed on the data of several paddy grown areas in Sri Lanka proved that ANN model (with MSE < 0.386) can be used with less computational time to predict the future paddy yield based on future climatic data [14]. An advanced application of ANN integrated with multiple linear regression (MLR) and penalized regression models for prediction of rice yield based on weather parameters at the west coast of India was presented by Das et al [15].…”

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

“…The research found that all analyzed models produced high-accuracy predictions (the MSE value ranged from 0.01 to 0.39 t•ha −1 ). However, the Levenberg-Marquardt and scaled conjugated gradient algorithms required fewer epochs and a shorter computation time [115]. Gonzalez-Sanchez et al [18], apart from the sum of precipitation, minimum and maximum temperature, relative humidity, and field location, also used solar radiation (in MJ/m 2 ) as an explanatory variable in the prediction of agricultural crops grown in Sinaloa (Western Mexico).…”