Abstract
In this paper, we have proposed a novel clustering-based approach for the prediction of the quality index of agricultural land. This paper aims to develop a system that can predict the quality index of the land based on the hyperspectral data acquired for the specified region over the period of 6 years. Dataset collected for this research consists of hyperspectral images of agricultural lands belonging to different geographical regions in India. Google earth engine platform has been used to collect the hyperspectral data of the Landsat 8 satellite. Autoencoder and k-means clustering algorithms are used for high-performance dimensionality reduction and clustering of the data respectively. Insurance agencies, NGOs, government bodies can get the benefits from the proposed methodology for client claim verification or for conducting agricultural surveys on large scale. As the data can be collected from a remote location, experts won’t need to visit the field each time.
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Kulkarni, P., Wyawahare, M., Karwande, A., Kolhe, T., Kamble, S., Joshi, A. (2022). Agricultural Field Analysis Using Satellite Hyperspectral Data and Autoencoder. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_31
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