Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network
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...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-05-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/11/10/1157 |
id |
doaj-a0dde86760214555ac321f4007223d9e |
---|---|
record_format |
Article |
spelling |
doaj-a0dde86760214555ac321f4007223d9e2020-11-24T21:28:00ZengMDPI AGRemote Sensing2072-42922019-05-011110115710.3390/rs11101157rs11101157Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder NetworkJ. Fuentes-Pacheco0J. Torres-Olivares1E. Roman-Rangel2S. Cervantes3P. Juarez-Lopez4J. Hermosillo-Valadez5J.M. Rendón-Mancha6CONACyT-Centro de Investigación en Ciencias, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, MexicoMaestría en Ciencias, Centro de Investigación en Ciencias, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, MexicoDigital Systems Department, Instituto Tecnologico Autonomo de Mexico, Mexico City 01080, MexicoDepartment of Computational Science and Engineering, Los Valles University Center of University of Guadalajara, Ameca, Jalisco 46600, MexicoFacultad de Ciencias Agropecuarias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, MexicoCentro de Investigación en Ciencias, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, MexicoCentro de Investigación en Ciencias, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, MexicoCrop 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.https://www.mdpi.com/2072-4292/11/10/1157convolutional neural networkcrop segmentation<i>Ficus carica</i>unmanned aerial vehicles |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
J. Fuentes-Pacheco J. Torres-Olivares E. Roman-Rangel S. Cervantes P. Juarez-Lopez J. Hermosillo-Valadez J.M. Rendón-Mancha |
spellingShingle |
J. Fuentes-Pacheco J. Torres-Olivares E. Roman-Rangel S. Cervantes P. Juarez-Lopez J. Hermosillo-Valadez J.M. Rendón-Mancha Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network Remote Sensing convolutional neural network crop segmentation <i>Ficus carica</i> unmanned aerial vehicles |
author_facet |
J. Fuentes-Pacheco J. Torres-Olivares E. Roman-Rangel S. Cervantes P. Juarez-Lopez J. Hermosillo-Valadez J.M. Rendón-Mancha |
author_sort |
J. Fuentes-Pacheco |
title |
Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network |
title_short |
Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network |
title_full |
Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network |
title_fullStr |
Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network |
title_full_unstemmed |
Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network |
title_sort |
fig plant segmentation from aerial images using a deep convolutional encoder-decoder network |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-05-01 |
description |
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. |
topic |
convolutional neural network crop segmentation <i>Ficus carica</i> unmanned aerial vehicles |
url |
https://www.mdpi.com/2072-4292/11/10/1157 |
work_keys_str_mv |
AT jfuentespacheco figplantsegmentationfromaerialimagesusingadeepconvolutionalencoderdecodernetwork AT jtorresolivares figplantsegmentationfromaerialimagesusingadeepconvolutionalencoderdecodernetwork AT eromanrangel figplantsegmentationfromaerialimagesusingadeepconvolutionalencoderdecodernetwork AT scervantes figplantsegmentationfromaerialimagesusingadeepconvolutionalencoderdecodernetwork AT pjuarezlopez figplantsegmentationfromaerialimagesusingadeepconvolutionalencoderdecodernetwork AT jhermosillovaladez figplantsegmentationfromaerialimagesusingadeepconvolutionalencoderdecodernetwork AT jmrendonmancha figplantsegmentationfromaerialimagesusingadeepconvolutionalencoderdecodernetwork |
_version_ |
1725972145368989696 |