Abstract
Agricultural sector plays an important role in the overall development of Bangladesh. Bangladesh is a densely populated country where the production of high-yielding crops is necessary. In Bangladesh, approximately 50% of the population are primarily employed in agriculture and more than 70% of the land area is used for cultivation. However, due to the traditional agricultural system, the country still lags behind to the best use of its resources. In our research, we investigate the presence of major soil nutrients using the hyperspectral images from Landsat-8 satellite of the northern regions of Bangladesh. In these regions, people are still using the ancient agricultural system despite being mostly dependent on agriculture. They are less likely to understand the soil quality, which consequently impacts on the production of crops. In this research, we capture 1.5K+ hyperspectral satellite images using GloVis/Earth Explorer from the northern regions of Bangladesh. Then, we apply the latest computer vision technologies, i.e., the ArcMap software to identify the major spatial properties. Later, we collect the actual soil ingredients of those locations from agriculture extension offices and government websites. Finally, we build a machine learning-based hybrid classification model where different features of satellite imagery are independent features and soil nutrients are dependent features. We also build a web tool for the agriculturists and government service providers to understand the soil quality well for better crop yielding.
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Riad, S. et al. (2022). Prediction of Soil Nutrients Using Hyperspectral Satellite Imaging. In: Hossain, S., Hossain, M.S., Kaiser, M.S., Majumder, S.P., Ray, K. (eds) Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021 . Lecture Notes in Networks and Systems, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-19-2445-3_12
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DOI: https://doi.org/10.1007/978-981-19-2445-3_12
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