A smart data-driven rapid method to recognize the strawberry maturity
In recent years, there have been many studies on the recognition of strawberry maturity, but there are still problems such as low recognition accuracy and expensive experimental instruments. These factors make their methods difficult for farmers to use. To solve these problems, we developed a fast,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-12-01
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| Series: | Information Processing in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317319300605 |
| _version_ | 1797813882980401152 |
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| author | Xiao-Qin Yue Zhen-Yu Shang Jia-Yi Yang Lan Huang Yong-Qian Wang |
| author_facet | Xiao-Qin Yue Zhen-Yu Shang Jia-Yi Yang Lan Huang Yong-Qian Wang |
| author_sort | Xiao-Qin Yue |
| collection | DOAJ |
| description | In recent years, there have been many studies on the recognition of strawberry maturity, but there are still problems such as low recognition accuracy and expensive experimental instruments. These factors make their methods difficult for farmers to use. To solve these problems, we developed a fast, non-destructive, accurate and convenient method for strawberry maturity identification using smartphones. In this paper, strawberry maturity is divided into three levels: mature, nearly-mature and immature. Considering the actual strawberry harvest process and postharvest handling, we focus on the differentiation between the mature and the nearly-mature ones to help farmers reduce possible damage in transit and improve profitability. We obtained the images of strawberries with different maturities at 535 nm and 670 nm wavelengths through a smartphone and got absorbance data by image processing based on the region of interest. The absorbance data were used to establish three maturity recognition models—i.e., multivariate linear, multivariate nonlinear and SoftMax regression classifier. The results showed that the multivariate nonlinear model had the highest identification accuracy (which is over 94%) in the greenhouse. Therefore, this method has considerable potential as a means for rapid recognition of strawberry maturity. |
| first_indexed | 2024-03-13T07:59:17Z |
| format | Article |
| id | doaj.art-84ba158592624ad890bd05c86a336caf |
| institution | Directory Open Access Journal |
| issn | 2214-3173 |
| language | English |
| last_indexed | 2024-03-13T07:59:17Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj.art-84ba158592624ad890bd05c86a336caf2023-06-02T00:02:05ZengElsevierInformation Processing in Agriculture2214-31732020-12-0174575584A smart data-driven rapid method to recognize the strawberry maturityXiao-Qin Yue0Zhen-Yu Shang1Jia-Yi Yang2Lan Huang3Yong-Qian Wang4College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCorresponding authors.; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCorresponding authors.; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaIn recent years, there have been many studies on the recognition of strawberry maturity, but there are still problems such as low recognition accuracy and expensive experimental instruments. These factors make their methods difficult for farmers to use. To solve these problems, we developed a fast, non-destructive, accurate and convenient method for strawberry maturity identification using smartphones. In this paper, strawberry maturity is divided into three levels: mature, nearly-mature and immature. Considering the actual strawberry harvest process and postharvest handling, we focus on the differentiation between the mature and the nearly-mature ones to help farmers reduce possible damage in transit and improve profitability. We obtained the images of strawberries with different maturities at 535 nm and 670 nm wavelengths through a smartphone and got absorbance data by image processing based on the region of interest. The absorbance data were used to establish three maturity recognition models—i.e., multivariate linear, multivariate nonlinear and SoftMax regression classifier. The results showed that the multivariate nonlinear model had the highest identification accuracy (which is over 94%) in the greenhouse. Therefore, this method has considerable potential as a means for rapid recognition of strawberry maturity.http://www.sciencedirect.com/science/article/pii/S2214317319300605Strawberry maturityRapid recognitionAbsorbanceMultiple nonlinear regressionSmartphone |
| spellingShingle | Xiao-Qin Yue Zhen-Yu Shang Jia-Yi Yang Lan Huang Yong-Qian Wang A smart data-driven rapid method to recognize the strawberry maturity Information Processing in Agriculture Strawberry maturity Rapid recognition Absorbance Multiple nonlinear regression Smartphone |
| title | A smart data-driven rapid method to recognize the strawberry maturity |
| title_full | A smart data-driven rapid method to recognize the strawberry maturity |
| title_fullStr | A smart data-driven rapid method to recognize the strawberry maturity |
| title_full_unstemmed | A smart data-driven rapid method to recognize the strawberry maturity |
| title_short | A smart data-driven rapid method to recognize the strawberry maturity |
| title_sort | smart data driven rapid method to recognize the strawberry maturity |
| topic | Strawberry maturity Rapid recognition Absorbance Multiple nonlinear regression Smartphone |
| url | http://www.sciencedirect.com/science/article/pii/S2214317319300605 |
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