Document Type

Conference Proceeding


Australian Society of Information and Communication Technologies in Agriculture


Faculty of Health, Engineering and Science


School of Computer and Security Science / eAgriculture Research Group




This article was originally published as: Ranjeet, T. , & Armstrong, L. (2014). An Artificial Neural Network for Predicting Crops Yield in Nepal. Proceedings of Asian Federation for Information Technology in Agriculture. (pp. 371-381). Perth, W.A. Australian Society of Information and Communication Technologies in Agriculture. Original article available here


This paper examines the application of artificial neural networks (ANNs) for predicting crop yields for an agricultural region in Nepal. The neural network algorithm has become an effective data mining tool and the outcome produced by this algorithm is considered to be less error prone than other computer science techniques. The backpropagation algorithm which iteratively finds a suitable weight value is considered for computing the error derivative. Agricultural data was collected from thirteen years from paddy field cultivation in the Siraha district, an eastern region in Nepal, and used for this investigation of neural networks. Additionally, climatic parameters including rainfall, maximum temperature and minimum temperature along with the fertilizer use were also used as input values. The experiment shows that the trained neural network produced a minimum error which indicated that the test model is capable of predicting crops yield in Nepal.

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