Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2602
Title: Towards Smart Farming: Accurate Prediction of Paddy Harvest and Rice Demand
Authors: Hashini Saranga, A. M
Weerakkody, W. A. N. D
Palliyaguru, S. T
Muthusinghe, R
Rankothge, W
Keywords: Smart Farming
Towards
Accurate
Prediction
Paddy Harvest
Rice Demand
Issue Date: 31-Jan-2019
Publisher: IEEE
Citation: Muthusinghe, Rashmi & Palliyaguru, Sachini & Weerakkody, W. & Saranga, A. & Rankothge, Windhya. (2018). Towards Smart Farming: Accurate Prediction of Paddy Harvest and Rice Demand. 1-6. 10.1109/R10-HTC.2018.8629843.
Series/Report no.: 2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC);
Abstract: Rice is the predominant staple food in Asian countries. It has a major impact on the social and economic development of these countries. Therefore, it is very important to keep the sustainability between paddy cultivation and consumer demand. Paddy crop yield and demand for rice of a country depend on numerous factors such as rainfall, humidity, citizen's life styles etc. Hence, the prediction of future harvest and demand is a complex process. There is a requirement for a platform that predicts on future harvest and demands based on all affecting factors. We have proposed a platform that targets the smart farming concepts for paddy, with following modules: (1) a prediction module to predict paddy harvest and (2) a prediction module to predict rice demand. We have developed the prediction modules using two machine learning algorithms: (1) Recurrent Neural Network (RNN) and (2) Long Short-Term Memory (LSTM). The performances of algorithms were evaluated using real data sets for the Sri Lankan context. Our results show that the prediction modules are giving accurate results in a short time.
URI: http://rda.sliit.lk/handle/123456789/2602
ISSN: 2572-7621
Appears in Collections:Department of Computer Systems Engineering-Scopes
Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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