Prediction of Potato Yield Based on Energy Inputs Using Artificial Neural Networks and C-sharp Under Saudi Arabia Conditions

Saad A. Al-Hamed and  Mohamed F. Wahby

Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia.

DOI : http://dx.doi.org/10.13005/bbra/2079

ABSTRACT: The objective of this study was to explore the relation between energy inputs and potato yield using artificial neural network (ANN) under Saudi Arabia conditions. Additionally, the extracted weights from ANN model were formulated using C-sharp language to develop interactive application for friendly and easy use. For this purpose, the energy use pattern was determined by collection data from two sources, the first source was actual field experiments in three sites belong to Riyadh region, Saudi Arabia and the second source from growers by using a face to face questionnaire method. The results indicated that total energy consumption and yield of potato production were different based on production pattern. In this study, for field experiment data, average the energy indices covering energy efficiency, specific energy and energy productivity were calculated at 2.25, 1.60 MJ/kg and 0.63 kg/MJ, respectively. For predicting of potato yield based on energy inputs, artificial neural network (ANN) with standard back propagation algorithm was employed. The results illustrated the ANN model with 6-15-22-1 architecture that had the best condition to the prediction of potato yield. With respect to ANN model, R², mean absolute error and mean relative error were computed as 0.704, 2.36 ton/ha and 5.59%, respectively in testing stage.  Moreover, contribution analysis was applied after training process of the ANN model. The results disclosed the water irrigation energy which had the highest contribution (24.75%) to potato yield among all inputs (machinery energy, diesel fuel energy, labor energy, chemicals energy and seeds energy). The developed C-sharp interactive application was tested and it can estimate potato yield. Soil and agronomy researchers, framers and agricultural engineers can use the developed C-sharp interactive application to explore the input variables that have more potential to increase potato yield on a farm.  It is user-friendly and could be run on Windows desktop without C-sharp environment.

KEYWORDS: Potato; Energy; artificial neural network; C-Sharp

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