A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

Estimation of crop damage plays a vital role in the management of fields in the agriculture sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a st...

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Autores principales:	Ali Mirzazadeh, Afshin Azizi, Yousef Abbaspour-Gilandeh, José Luis Hernández-Hernández, Mario Hernández-Hernández, Iván Gallardo-Bernal
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	rapeseed classification damaged crops deep neural networks Agriculture S
Acceso en línea:	https://doaj.org/article/efa5e4f3ae07474f84a8931e6f821b95
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spelling	oai:doaj.org-article:efa5e4f3ae07474f84a8931e6f821b952021-11-25T16:12:40ZA Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm10.3390/agronomy111123642073-4395https://doaj.org/article/efa5e4f3ae07474f84a8931e6f821b952021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2364https://doaj.org/toc/2073-4395Estimation of crop damage plays a vital role in the management of fields in the agriculture sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds’ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of deep learning-based models to classify other damaged crops.Ali MirzazadehAfshin AziziYousef Abbaspour-GilandehJosé Luis Hernández-HernándezMario Hernández-HernándezIván Gallardo-BernalMDPI AGarticlerapeseedclassificationdamaged cropsdeep neural networksAgricultureSENAgronomy, Vol 11, Iss 2364, p 2364 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rapeseed classification damaged crops deep neural networks Agriculture S
spellingShingle	rapeseed classification damaged crops deep neural networks Agriculture S Ali Mirzazadeh Afshin Azizi Yousef Abbaspour-Gilandeh José Luis Hernández-Hernández Mario Hernández-Hernández Iván Gallardo-Bernal A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm
description	Estimation of crop damage plays a vital role in the management of fields in the agriculture sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds’ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of deep learning-based models to classify other damaged crops.
format	article
author	Ali Mirzazadeh Afshin Azizi Yousef Abbaspour-Gilandeh José Luis Hernández-Hernández Mario Hernández-Hernández Iván Gallardo-Bernal
author_facet	Ali Mirzazadeh Afshin Azizi Yousef Abbaspour-Gilandeh José Luis Hernández-Hernández Mario Hernández-Hernández Iván Gallardo-Bernal
author_sort	Ali Mirzazadeh
title	A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm
title_short	A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm
title_full	A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm
title_fullStr	A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm
title_full_unstemmed	A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm
title_sort	novel technique for classifying bird damage to rapeseed plants based on a deep learning algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/efa5e4f3ae07474f84a8931e6f821b95
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A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

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