In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology
Abstract Rapid and accurate assessment of the citrus ripening stage in the field is important for determining harvest timing and improving industrial economic efficiency; however, the lack of effective nondestructive detection methods in the current orchard leads to flaws in ripening stage assessmen...
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| Format: | Article |
| Language: | English |
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BMC
2025-06-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-025-01354-z |
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| author | Qi Wang Jinzhu Lu Yuanhong Wang Fajun Miao Senping Liu Qiyang Shui Junfeng Gao Yingwang Gao |
| author_facet | Qi Wang Jinzhu Lu Yuanhong Wang Fajun Miao Senping Liu Qiyang Shui Junfeng Gao Yingwang Gao |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | Abstract Rapid and accurate assessment of the citrus ripening stage in the field is important for determining harvest timing and improving industrial economic efficiency; however, the lack of effective nondestructive detection methods in the current orchard leads to flaws in ripening stage assessment, which affects harvesting decisions. To solve this problem, this study utilized hyperspectral technology to collect data from 22 fruit trees in an orchard (in the range of 400–1000 nm) and explored the effectiveness of five regions of interest selection methods (x-axis, y-axis, four-quadrant, threshold segmentation, and raw) for the delineation of the citrus ripening stage. The data quality was enhanced via wavelet transform (WT)-multiple scattering correction (MSC) preprocessing, and the effective wavelengths were extracted via the successive projections algorithm (SPA). On the basis of these wavelengths, backpropagation neural network (BP) and convolutional neural network (CNN) models were built for maturity prediction. The results show that the x-axis region of interest selection method outperforms the other methods, and the SPA-BP model based on this method performs best. An accuracy of 99.19% for the correction set and 100% for the prediction set was achieved when only 0.03% of the wavelength was used. This groundbreaking study highlights the significant potential of hyperspectral technology for in situ assessment of citrus ripening stages. Furthermore, it offers crucial technical support and serves as a valuable reference for the advancement of precision agriculture. |
| format | Article |
| id | doaj-art-2a90ead1df104fc7b823e363647d56dd |
| institution | Kabale University |
| issn | 1746-4811 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-2a90ead1df104fc7b823e363647d56dd2025-08-20T03:26:43ZengBMCPlant Methods1746-48112025-06-0121111310.1186/s13007-025-01354-zIn situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technologyQi Wang0Jinzhu Lu1Yuanhong Wang2Fajun Miao3Senping Liu4Qiyang Shui5Junfeng Gao6Yingwang Gao7Modern Agricultural Equipment Research Institute, Xihua UniversitySchool of Mechanical Engineering, Xihua UniversityModern Agricultural Equipment Research Institute, Xihua UniversityModern Agricultural Equipment Research Institute, Xihua UniversityModern Agricultural Equipment Research Institute, Xihua UniversityModern Agricultural Equipment Research Institute, Xihua UniversityDepartment of Computer Science, University of AberdeenUK Agri-Tech CentreAbstract Rapid and accurate assessment of the citrus ripening stage in the field is important for determining harvest timing and improving industrial economic efficiency; however, the lack of effective nondestructive detection methods in the current orchard leads to flaws in ripening stage assessment, which affects harvesting decisions. To solve this problem, this study utilized hyperspectral technology to collect data from 22 fruit trees in an orchard (in the range of 400–1000 nm) and explored the effectiveness of five regions of interest selection methods (x-axis, y-axis, four-quadrant, threshold segmentation, and raw) for the delineation of the citrus ripening stage. The data quality was enhanced via wavelet transform (WT)-multiple scattering correction (MSC) preprocessing, and the effective wavelengths were extracted via the successive projections algorithm (SPA). On the basis of these wavelengths, backpropagation neural network (BP) and convolutional neural network (CNN) models were built for maturity prediction. The results show that the x-axis region of interest selection method outperforms the other methods, and the SPA-BP model based on this method performs best. An accuracy of 99.19% for the correction set and 100% for the prediction set was achieved when only 0.03% of the wavelength was used. This groundbreaking study highlights the significant potential of hyperspectral technology for in situ assessment of citrus ripening stages. Furthermore, it offers crucial technical support and serves as a valuable reference for the advancement of precision agriculture.https://doi.org/10.1186/s13007-025-01354-z‘Shiranui’ mandarinHyperspectralRegion of interestRipening stageSuccessive projections algorithm |
| spellingShingle | Qi Wang Jinzhu Lu Yuanhong Wang Fajun Miao Senping Liu Qiyang Shui Junfeng Gao Yingwang Gao In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology Plant Methods ‘Shiranui’ mandarin Hyperspectral Region of interest Ripening stage Successive projections algorithm |
| title | In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology |
| title_full | In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology |
| title_fullStr | In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology |
| title_full_unstemmed | In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology |
| title_short | In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology |
| title_sort | in situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology |
| topic | ‘Shiranui’ mandarin Hyperspectral Region of interest Ripening stage Successive projections algorithm |
| url | https://doi.org/10.1186/s13007-025-01354-z |
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