Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks

Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better qua...

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Autores principales:	Rolando Miragaia, Francisco Chávez, Josefa Díaz, Antonio Vivas, Maria Henar Prieto, Maria José Moñino
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	agriculture digitalization precision agriculture computer vision plum orchard <i>Prunus salicina</i> Agriculture S
Acceso en línea:	https://doaj.org/article/25264bca3d424ac4bbd79f8bd7400425
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id	oai:doaj.org-article:25264bca3d424ac4bbd79f8bd7400425
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spelling	oai:doaj.org-article:25264bca3d424ac4bbd79f8bd74004252021-11-25T16:12:26ZPlum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks10.3390/agronomy111123532073-4395https://doaj.org/article/25264bca3d424ac4bbd79f8bd74004252021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2353https://doaj.org/toc/2073-4395Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness, three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety.Rolando MiragaiaFrancisco ChávezJosefa DíazAntonio VivasMaria Henar PrietoMaria José MoñinoMDPI AGarticleagriculture digitalizationprecision agriculturecomputer visionplum orchard<i>Prunus salicina</i>AgricultureSENAgronomy, Vol 11, Iss 2353, p 2353 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	agriculture digitalization precision agriculture computer vision plum orchard <i>Prunus salicina</i> Agriculture S
spellingShingle	agriculture digitalization precision agriculture computer vision plum orchard <i>Prunus salicina</i> Agriculture S Rolando Miragaia Francisco Chávez Josefa Díaz Antonio Vivas Maria Henar Prieto Maria José Moñino Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks
description	Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness, three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety.
format	article
author	Rolando Miragaia Francisco Chávez Josefa Díaz Antonio Vivas Maria Henar Prieto Maria José Moñino
author_facet	Rolando Miragaia Francisco Chávez Josefa Díaz Antonio Vivas Maria Henar Prieto Maria José Moñino
author_sort	Rolando Miragaia
title	Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks
title_short	Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks
title_full	Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks
title_fullStr	Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks
title_full_unstemmed	Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks
title_sort	plum ripeness analysis in real environments using deep learning with convolutional neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/25264bca3d424ac4bbd79f8bd7400425
work_keys_str_mv	AT rolandomiragaia plumripenessanalysisinrealenvironmentsusingdeeplearningwithconvolutionalneuralnetworks AT franciscochavez plumripenessanalysisinrealenvironmentsusingdeeplearningwithconvolutionalneuralnetworks AT josefadiaz plumripenessanalysisinrealenvironmentsusingdeeplearningwithconvolutionalneuralnetworks AT antoniovivas plumripenessanalysisinrealenvironmentsusingdeeplearningwithconvolutionalneuralnetworks AT mariahenarprieto plumripenessanalysisinrealenvironmentsusingdeeplearningwithconvolutionalneuralnetworks AT mariajosemonino plumripenessanalysisinrealenvironmentsusingdeeplearningwithconvolutionalneuralnetworks
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Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks

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