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Proceedings Paper

Using remote sensing satellite data and artificial neural network for prediction of potato yield in Bangladesh
Author(s): Kawsar Akhand; Mohammad Nizamuddin; Leonid Roytman; Felix Kogan
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Paper Abstract

Potato is one of the staple foods and cash crops in Bangladesh. It is widely cultivated in all of the districts and ranks second after rice in production. Bangladesh is the fourth largest potato producer in Asia and is among the world’s top 15 potato producing countries. The weather condition for potato cultivation is favorable during the sowing, growing and harvesting period. It is a winter crop and is cultivated during the period of November to March. Bangladesh is mainly an agricultural based country with respect to agriculture’s contribution to GDP, employment and consumption. Potato is a prominent crop in consideration of production, its internal demand and economic value. Bangladesh has a big economic activities related to potato cultivation and marketing, especially the economic relations among farmers, traders, stockers and cold storage owners. Potato yield prediction before harvest is an important issue for the Government and the stakeholders in managing and controlling the potato market. Advanced very high resolution radiometer (AVHRR) based satellite data product vegetation health indices VCI (vegetation condition index) and TCI (temperature condition index) are used as predictors for early prediction. Artificial neural network (ANN) is used to develop a prediction model. The simulated result from this model is encouraging and the error of prediction is less than 10%.

Paper Details

Date Published: 19 September 2016
PDF: 15 pages
Proc. SPIE 9975, Remote Sensing and Modeling of Ecosystems for Sustainability XIII, 997508 (19 September 2016); doi: 10.1117/12.2237214
Show Author Affiliations
Kawsar Akhand, The City College of New York (United States)
Mohammad Nizamuddin, The City College of New York (United States)
Leonid Roytman, The City College of New York (United States)
Felix Kogan, National Oceanic and Atmospheric Administration (United States)

Published in SPIE Proceedings Vol. 9975:
Remote Sensing and Modeling of Ecosystems for Sustainability XIII
Wei Gao; Ni-Bin Chang, Editor(s)

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