Next Article in Journal
Context Aggregation Network for Semantic Labeling in Aerial Images
Previous Article in Journal
Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images
Previous Article in Special Issue
A Stacked Fully Convolutional Networks with Feature Alignment Framework for Multi-Label Land-cover Segmentation
Article Menu

Export Article

Open AccessArticle

Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

1
CONACyT-Centro de Investigación en Ciencias, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico
2
Maestría en Ciencias, Centro de Investigación en Ciencias, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico
3
Digital Systems Department, Instituto Tecnologico Autonomo de Mexico, Mexico City 01080, Mexico
4
Department of Computational Science and Engineering, Los Valles University Center of University of Guadalajara, Ameca, Jalisco 46600, Mexico
5
Facultad de Ciencias Agropecuarias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico
6
Centro de Investigación en Ciencias, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1157; https://doi.org/10.3390/rs11101157
Received: 1 April 2019 / Revised: 4 May 2019 / Accepted: 7 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue Convolutional Neural Networks Applications in Remote Sensing)
  |  
PDF [11023 KB, uploaded 15 May 2019]
  |  

Abstract

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms. View Full-Text
Keywords: convolutional neural network; crop segmentation; Ficus carica; unmanned aerial vehicles convolutional neural network; crop segmentation; Ficus carica; unmanned aerial vehicles
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Fuentes-Pacheco, J.; Torres-Olivares, J.; Roman-Rangel, E.; Cervantes, S.; Juarez-Lopez, P.; Hermosillo-Valadez, J.; Rendón-Mancha, J. Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network. Remote Sens. 2019, 11, 1157.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top