Using remote sensing satellite data and artificial neural network for prediction of potato yield in Bangladesh

Kawsar Akhand; Mohammad Nizamuddin; Leonid Roytman; Felix Kogan

doi:10.1117/12.2237214

19 September 2016 Using remote sensing satellite data and artificial neural network for prediction of potato yield in Bangladesh

Kawsar Akhand, Mohammad Nizamuddin, Leonid Roytman, Felix Kogan

Author Affiliations +

Proceedings Volume 9975, Remote Sensing and Modeling of Ecosystems for Sustainability XIII; 997508 (2016) https://doi.org/10.1117/12.2237214
Event: SPIE Optical Engineering + Applications, 2016, San Diego, California, United States

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%.

Conference Presentation

Citation Download Citation

Kawsar Akhand, Mohammad Nizamuddin, Leonid Roytman, and Felix Kogan "Using remote sensing satellite data and artificial neural network for prediction of potato yield in Bangladesh", Proc. SPIE 9975, Remote Sensing and Modeling of Ecosystems for Sustainability XIII, 997508 (19 September 2016); https://doi.org/10.1117/12.2237214

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