Title
Semantic Segmentation of Weeds and Crops in Multispectral Images by Using a Convolutional Neural Networks Based on U-Net
Date Issued
01 January 2020
Access level
metadata only access
Resource Type
Book Series
Author(s)
Chicchón Apaza M.Á.
Garrido R.P.A.
Abstract
A first step in the process of automating weed removal in precision agriculture is the semantic segmentation of crops, weeds and soil. Deep learning techniques based on convolutional neural networks are successfully applied today and one of the most popular network architectures in semantic segmentation problems is U-Net. In this article, the variants in the U-Net architecture were evaluated based on the aggregation of residual and recurring blocks to improve their performance. For training and testing, a set of data available on the Internet was used, consisting of 60 multispectral images with unbalanced pixels, so techniques were applied to increase and balance the data. Experimental results show a slight increase in quality metrics compared to the classic U-Net architecture.
Start page
473
End page
485
Volume
1194 CCIS
Scopus EID
2-s2.0-85082396508
ISBN
9783030425197
Source
Communications in Computer and Information Science
Resource of which it is part
Communications in Computer and Information Science
ISSN of the container
18650929
Source funding
Birla Institute of Scientific Research
Sources of information: Directorio de Producción Científica Scopus