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2021 | OriginalPaper | Chapter

A Technique to Classify Sugarcane Crop from Sentinel-2 Satellite Imagery Using U-Net Architecture

Authors : Shyamal Virnodkar, V. K. Pachghare, Sagar Murade

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

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Abstract

Satellite imagery data collected from various modern and older versions of satellites discover its applications in a variety of domains. One of the domains with great importance is the agriculture domain. Satellite imagery data can be significantly used in agricultural applications to increase the precision and efficiency of farming. These images are of great importance in applications like disease detection, crop classification, weather monitoring and farmland usage. In this paper, we propose a technique to classify sugarcane crops from the satellite imagery utilizing a supervised machine learning approach. Unlike unsupervised models, this technique relies on the ground truth data collected from the farm to train, test, and validate the model. The ground samples contain four stages, germination, tillering, grand growth, and maturity, of the sugarcane growth cycle. This collected information acts as an input to the U-Net architecture which will extract the features unique to the sugarcane field and further classify the sugarcane crop.

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Metadata
Title
A Technique to Classify Sugarcane Crop from Sentinel-2 Satellite Imagery Using U-Net Architecture
Authors
Shyamal Virnodkar
V. K. Pachghare
Sagar Murade
Copyright Year
2021
Publisher
Springer Singapore
Book
Progress in Advanced Computing and Intelligent Engineering

Print ISBN: 978-981-15-6352-2

Electronic ISBN: 978-981-15-6353-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-6353-9_29