Title

Rice crop yield prediction using artificial neural networks

Document Type

Conference Proceeding

Publisher

IEEE

School

School of Science

RAS ID

23203

Comments

Originally published as:

Gandhi, N., Petkar, O., & Armstrong, L. J. (2016). Rice crop yield prediction using artificial neural networks. In Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2016 IEEE(pp. 105-110). IEEE.

Original available here

Abstract

Rice crop production contributes to the food security of India, more than 40% to overall crop production. Its production is reliant on favorable climatic conditions. Variability from season to season is detrimental to the farmer's income and livelihoods. Improving the ability of farmers to predict crop productivity in under different climatic scenarios, can assist farmers and other stakeholders in making important decisions in terms of agronomy and crop choice. This study aimed to use neural networks to predict rice production yield and investigate the factors affecting the rice crop yield for various districts of Maharashtra state in India. Data were sourced from publicly available Indian Government's records for 27 districts of Maharashtra state, India. The parameters considered for the present study were precipitation, minimum temperature, average temperature, maximum temperature and reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. The dataset was processed using WEKA tool. A Multilayer Perceptron Neural Network was developed. Cross validation method was used to validate the data. The results showed the accuracy of 97.5% with a sensitivity of 96.3 and specificity of 98.1. Further, mean absolute error, root mean squared error, relative absolute error and root relative squared error were calculated for the present study. The study dataset was also executed using Knowledge Flow of the WEKA tool. The performance of the classifier is visually summarized using ROC curve.

DOI

10.1109/TIAR.2016.7801222

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