Detection of Fundamental Quality Traits of Winter Jujube Based on Computer Vision and Deep Learning
Winter jujube (<i>Ziziphus jujuba</i> Mill. cv. Dongzao) has been cultivated in China for a long time and has a richly abundant history, whose maturity grade determined different postharvest qualities. Traditional methods for identifying the fundamental quality of winter jujube are known...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/8/2095 |
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| author | Zhaojun Ban Chenyu Fang Lingling Liu Zhengbao Wu Cunkun Chen Yi Zhu |
| author_facet | Zhaojun Ban Chenyu Fang Lingling Liu Zhengbao Wu Cunkun Chen Yi Zhu |
| author_sort | Zhaojun Ban |
| collection | DOAJ |
| description | Winter jujube (<i>Ziziphus jujuba</i> Mill. cv. Dongzao) has been cultivated in China for a long time and has a richly abundant history, whose maturity grade determined different postharvest qualities. Traditional methods for identifying the fundamental quality of winter jujube are known to be time-consuming and labor-intensive, resulting in significant difficulties for winter jujube resource management. The applications of deep learning in this regard will help manufacturers and orchard workers quickly identify fundamental quality information. In our study, the best fundamental quality of winter jujube from the correlation between maturity and fundamental quality was determined by testing three simple physicochemical indexes: total soluble solids (TSS), total acid (TA) and puncture force of fruit at five maturity stages which classified by the color and appearance. The results showed that the fully red fruits (the 4th grade) had the optimal eating quality parameter. Additionally, five different maturity grades of winter jujube were photographed as datasets and used the ResNet-50 model and the iResNet-50 model for training. And the iResNet-50 model was improved to overlap double residuals in the first Main Stage, with an accuracy of 98.35%, a precision of 98.40%, a recall of 98.35%, and a F1 score of 98.36%, which provided an important basis for automatic fundamental quality detection of winter jujube. This study provided ideas for fundamental quality classification of winter jujube during harvesting, fundamental quality screening of winter jujube in assembly line production, and real-time monitoring of winter jujube during transportation and storage. |
| first_indexed | 2024-03-11T00:11:36Z |
| format | Article |
| id | doaj.art-7cdda7917a254dfc84410d3451ad51ed |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T00:11:36Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-7cdda7917a254dfc84410d3451ad51ed2023-11-18T23:54:57ZengMDPI AGAgronomy2073-43952023-08-01138209510.3390/agronomy13082095Detection of Fundamental Quality Traits of Winter Jujube Based on Computer Vision and Deep LearningZhaojun Ban0Chenyu Fang1Lingling Liu2Zhengbao Wu3Cunkun Chen4Yi Zhu5Zhejiang Provincial Key Laboratory of Chemical and Biological Processing Technology of Farm Products, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaZhejiang Provincial Key Laboratory of Chemical and Biological Processing Technology of Farm Products, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaZhejiang Provincial Key Laboratory of Chemical and Biological Processing Technology of Farm Products, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaEconomic Forest Research Institute, Xinjiang Academy of Forestry Sciences, Urumqi 830000, ChinaInstitute of Agricultural Products Preservation and Processing Technology, National Engineering Technology Research Center for Preservation of Agriculture Product, Tianjin Academy of Agricultural Sciences, Tianjin 300384, ChinaAksu Youneng Agricultural Technology Co., Ltd., Aksu 843001, ChinaWinter jujube (<i>Ziziphus jujuba</i> Mill. cv. Dongzao) has been cultivated in China for a long time and has a richly abundant history, whose maturity grade determined different postharvest qualities. Traditional methods for identifying the fundamental quality of winter jujube are known to be time-consuming and labor-intensive, resulting in significant difficulties for winter jujube resource management. The applications of deep learning in this regard will help manufacturers and orchard workers quickly identify fundamental quality information. In our study, the best fundamental quality of winter jujube from the correlation between maturity and fundamental quality was determined by testing three simple physicochemical indexes: total soluble solids (TSS), total acid (TA) and puncture force of fruit at five maturity stages which classified by the color and appearance. The results showed that the fully red fruits (the 4th grade) had the optimal eating quality parameter. Additionally, five different maturity grades of winter jujube were photographed as datasets and used the ResNet-50 model and the iResNet-50 model for training. And the iResNet-50 model was improved to overlap double residuals in the first Main Stage, with an accuracy of 98.35%, a precision of 98.40%, a recall of 98.35%, and a F1 score of 98.36%, which provided an important basis for automatic fundamental quality detection of winter jujube. This study provided ideas for fundamental quality classification of winter jujube during harvesting, fundamental quality screening of winter jujube in assembly line production, and real-time monitoring of winter jujube during transportation and storage.https://www.mdpi.com/2073-4395/13/8/2095deep learningwinter jujubefundamental qualitymaturity gradingconvolutional neural network |
| spellingShingle | Zhaojun Ban Chenyu Fang Lingling Liu Zhengbao Wu Cunkun Chen Yi Zhu Detection of Fundamental Quality Traits of Winter Jujube Based on Computer Vision and Deep Learning Agronomy deep learning winter jujube fundamental quality maturity grading convolutional neural network |
| title | Detection of Fundamental Quality Traits of Winter Jujube Based on Computer Vision and Deep Learning |
| title_full | Detection of Fundamental Quality Traits of Winter Jujube Based on Computer Vision and Deep Learning |
| title_fullStr | Detection of Fundamental Quality Traits of Winter Jujube Based on Computer Vision and Deep Learning |
| title_full_unstemmed | Detection of Fundamental Quality Traits of Winter Jujube Based on Computer Vision and Deep Learning |
| title_short | Detection of Fundamental Quality Traits of Winter Jujube Based on Computer Vision and Deep Learning |
| title_sort | detection of fundamental quality traits of winter jujube based on computer vision and deep learning |
| topic | deep learning winter jujube fundamental quality maturity grading convolutional neural network |
| url | https://www.mdpi.com/2073-4395/13/8/2095 |
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