A multi-division convolutional neural network-based plant identification system
Background Plants have an important place in the life of all living things. Today, there is a risk of extinction for many plant species due to climate change and its environmental impact. Therefore, researchers have conducted various studies with the aim of protecting the diversity of the planet’s p...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-05-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-572.pdf |
| _version_ | 1818946545596235776 |
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| author | Muammer Turkoglu Muzaffer Aslan Ali Arı Zeynep Mine Alçin Davut Hanbay |
| author_facet | Muammer Turkoglu Muzaffer Aslan Ali Arı Zeynep Mine Alçin Davut Hanbay |
| author_sort | Muammer Turkoglu |
| collection | DOAJ |
| description | Background Plants have an important place in the life of all living things. Today, there is a risk of extinction for many plant species due to climate change and its environmental impact. Therefore, researchers have conducted various studies with the aim of protecting the diversity of the planet’s plant life. Generally, research in this area is aimed at determining plant species and diseases, with works predominantly based on plant images. Advances in deep learning techniques have provided very successful results in this field, and have become widely used in research studies to identify plant species. Methods In this paper, a Multi-Division Convolutional Neural Network (MD-CNN)-based plant recognition system was developed in order to address an agricultural problem related to the classification of plant species. In the proposed system, we divide plant images into equal nxn-sized pieces, and then deep features are extracted for each piece using a Convolutional Neural Network (CNN). For each part of the obtained deep features, effective features are selected using the Principal Component Analysis (PCA) algorithm. Finally, the obtained effective features are combined and classification conducted using the Support Vector Machine (SVM) method. Results In order to test the performance of the proposed deep-based system, eight different plant datasets were used: Flavia, Swedish, ICL, Foliage, Folio, Flower17, Flower102, and LeafSnap. According to the results of these experimental studies, 100% accuracy scores were achieved for the Flavia, Swedish, and Folio datasets, whilst the ICL, Foliage, Flower17, Flower102, and LeafSnap datasets achieved results of 99.77%, 99.93%, 97.87%, 98.03%, and 94.38%, respectively. |
| first_indexed | 2024-12-20T08:16:43Z |
| format | Article |
| id | doaj.art-1b05cef78afa4c00bcda110d8b7ebeae |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-20T08:16:43Z |
| publishDate | 2021-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-1b05cef78afa4c00bcda110d8b7ebeae2022-12-21T19:47:06ZengPeerJ Inc.PeerJ Computer Science2376-59922021-05-017e57210.7717/peerj-cs.572A multi-division convolutional neural network-based plant identification systemMuammer Turkoglu0Muzaffer Aslan1Ali Arı2Zeynep Mine Alçin3Davut Hanbay4Faculty of Engineering, Department of Software Engineering, Samsun University, Samsun, TurkeyEngineering Faculty, Electrical and Electronics Engineering Department, Bingol University, Bingöl, TurkeyEngineering Faculty, Computer Engineering Department, Inonu University, Malatya, TurkeyVedat Topçuoğlu Anatolian Vocational High School, Electrical and Electronics Department, Gaziantep, TurkeyEngineering Faculty, Computer Engineering Department, Inonu University, Malatya, TurkeyBackground Plants have an important place in the life of all living things. Today, there is a risk of extinction for many plant species due to climate change and its environmental impact. Therefore, researchers have conducted various studies with the aim of protecting the diversity of the planet’s plant life. Generally, research in this area is aimed at determining plant species and diseases, with works predominantly based on plant images. Advances in deep learning techniques have provided very successful results in this field, and have become widely used in research studies to identify plant species. Methods In this paper, a Multi-Division Convolutional Neural Network (MD-CNN)-based plant recognition system was developed in order to address an agricultural problem related to the classification of plant species. In the proposed system, we divide plant images into equal nxn-sized pieces, and then deep features are extracted for each piece using a Convolutional Neural Network (CNN). For each part of the obtained deep features, effective features are selected using the Principal Component Analysis (PCA) algorithm. Finally, the obtained effective features are combined and classification conducted using the Support Vector Machine (SVM) method. Results In order to test the performance of the proposed deep-based system, eight different plant datasets were used: Flavia, Swedish, ICL, Foliage, Folio, Flower17, Flower102, and LeafSnap. According to the results of these experimental studies, 100% accuracy scores were achieved for the Flavia, Swedish, and Folio datasets, whilst the ICL, Foliage, Flower17, Flower102, and LeafSnap datasets achieved results of 99.77%, 99.93%, 97.87%, 98.03%, and 94.38%, respectively.https://peerj.com/articles/cs-572.pdfPlant Identification System Deep features Support Vector Machine Principal component analysis Division process |
| spellingShingle | Muammer Turkoglu Muzaffer Aslan Ali Arı Zeynep Mine Alçin Davut Hanbay A multi-division convolutional neural network-based plant identification system PeerJ Computer Science Plant Identification System Deep features Support Vector Machine Principal component analysis Division process |
| title | A multi-division convolutional neural network-based plant identification system |
| title_full | A multi-division convolutional neural network-based plant identification system |
| title_fullStr | A multi-division convolutional neural network-based plant identification system |
| title_full_unstemmed | A multi-division convolutional neural network-based plant identification system |
| title_short | A multi-division convolutional neural network-based plant identification system |
| title_sort | multi division convolutional neural network based plant identification system |
| topic | Plant Identification System Deep features Support Vector Machine Principal component analysis Division process |
| url | https://peerj.com/articles/cs-572.pdf |
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