Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling
Chlorophyll content in date leaves is critical for fruit quality and yield. Traditional detection methods are usually complex and expensive. This study proposes a rapid detection method for chlorophyll content using smartphone images and machine learning and deep learning models. The SPAD values and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2545 |
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| author | Qi Wang Ziyan Shi Kaiyao Hou Ning Yan Cuiyun Wu Xu Li |
| author_facet | Qi Wang Ziyan Shi Kaiyao Hou Ning Yan Cuiyun Wu Xu Li |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | Chlorophyll content in date leaves is critical for fruit quality and yield. Traditional detection methods are usually complex and expensive. This study proposes a rapid detection method for chlorophyll content using smartphone images and machine learning and deep learning models. The SPAD values and RGB images of Xinjiang date palm were collected. The RGB images were preprocessed and their color features were extracted using Python and OpenCV. Through correlation analysis, 21 color features highly correlated with chlorophyll content were selected and downscaled with principal component analysis. Models including SVR, RVM, CNN, CNN-SVR, and CNN-RVM were used for prediction. Among them, the CNN-SVR model showed the most stable performance with R<sup>2</sup> values of 72.21% and 77.44% on the training and validation sets, respectively, which outperformed the other models. The proposed method is simple, cost-effective, and highly accurate, providing a novel technical approach for accurate management and health monitoring in the date industry. This method has the potential for wide application. |
| format | Article |
| id | doaj-art-750a97cbf0bc45cabba7bef926085c9e |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-750a97cbf0bc45cabba7bef926085c9e2025-08-20T02:25:04ZengMDPI AGSensors1424-82202025-04-01258254510.3390/s25082545Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid ModelingQi Wang0Ziyan Shi1Kaiyao Hou2Ning Yan3Cuiyun Wu4Xu Li5College of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaChlorophyll content in date leaves is critical for fruit quality and yield. Traditional detection methods are usually complex and expensive. This study proposes a rapid detection method for chlorophyll content using smartphone images and machine learning and deep learning models. The SPAD values and RGB images of Xinjiang date palm were collected. The RGB images were preprocessed and their color features were extracted using Python and OpenCV. Through correlation analysis, 21 color features highly correlated with chlorophyll content were selected and downscaled with principal component analysis. Models including SVR, RVM, CNN, CNN-SVR, and CNN-RVM were used for prediction. Among them, the CNN-SVR model showed the most stable performance with R<sup>2</sup> values of 72.21% and 77.44% on the training and validation sets, respectively, which outperformed the other models. The proposed method is simple, cost-effective, and highly accurate, providing a novel technical approach for accurate management and health monitoring in the date industry. This method has the potential for wide application.https://www.mdpi.com/1424-8220/25/8/2545chlorophyll contentjujube leavessmartphone imagesSPADprecision agriculture |
| spellingShingle | Qi Wang Ziyan Shi Kaiyao Hou Ning Yan Cuiyun Wu Xu Li Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling Sensors chlorophyll content jujube leaves smartphone images SPAD precision agriculture |
| title | Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling |
| title_full | Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling |
| title_fullStr | Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling |
| title_full_unstemmed | Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling |
| title_short | Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling |
| title_sort | smartphone based spad value estimation for jujube leaves using machine learning a study on rgb feature extraction and hybrid modeling |
| topic | chlorophyll content jujube leaves smartphone images SPAD precision agriculture |
| url | https://www.mdpi.com/1424-8220/25/8/2545 |
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