Home⇛Science and Technology Journal⇛vol. 7 no. 1 (2017)

Predicting Rice Yield from Multi-Temporal Satellite Data Using Artificial Neural Network

Nathaniel R. Alibuyog | Leah M. Tute | Rodel C. Cabugon

Discipline: engineering (non-specific)

 

Abstract:

Monitoring the growth of rice and forecasting its yield before harvest season is very important for crop and food management. Remote sensing images are capable of identifying crop health as well as predicting its yield. This study explored the potentials of the artificial neural network (ANN) model for developing rice yield prediction systems using multi-temporal satellite data. The study made use of the 16-day composite TERRA MODIS satellite images downloaded from the internet from November 2010 to April 2011 to predict rice yield in the province of Ilocos Norte. Two ANN rice yield prediction models, namely RiceMod3 and RiceMod5, were developed. The results showed that RiceMod3 and RiceMod5 were quite efficient in capturing the complex relationship between rice yield and crop spectral signature with R2 values of 0.542 and 0.732, respectively. The ability of RiceMod3 to reasonably forecast the expected rice yield some time ahead of the harvesting date provide opportunities for a farm manager to make decisions before harvest. As such, it may prove useful to use the model in providing farm advisories. The RiceMod5 on the other hand may prove useful in providing timely prediction of crop yield over large areas and can be used as an alternate method for crop yield survey.



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