Performance Analysis of Deep Learning Classification for Agriculture Applications Using Sentinel-2 Data

Gurwinder Singh

Nowadays, deep learning algorithms are becoming more popular due to the presence of trained models and one-time processing. However, the deep learning model required a large amount of computation time and needs to be tested in different regions for different applications. In the present work, the deep learning algorithm has been tested over agricultural land (over a part of Punjab state, India) using Sentinel-2 imagery. The major classes considered in the present analysis are vegetation area, water, and buildup area. The statistical results have shown that more than 80% of accuracy has been obtained using a deep learning algorithm.



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