Inversion of citrus SPAD value and leaf water content by combining feature selection and ensemble learning algorithm using UAV remote sensing images
Soil and Plant Analyzer Development (SPAD) value and leaf water content (LWC) are critical physiological parameters for agricultural irrigation and growth monitoring in late-maturing citrus. Accurate monitoring of citrus SPAD value and LWC is of great significance for guiding precision irrigation, i...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425002380 |
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| author | Quanshan Liu Fei Chen Ningbo Cui Zongjun Wu Xiuliang Jin Shidan Zhu Shouzheng Jiang Daozhi Gong Shunsheng Zheng Lu Zhao Zhihui Wang |
| author_facet | Quanshan Liu Fei Chen Ningbo Cui Zongjun Wu Xiuliang Jin Shidan Zhu Shouzheng Jiang Daozhi Gong Shunsheng Zheng Lu Zhao Zhihui Wang |
| author_sort | Quanshan Liu |
| collection | DOAJ |
| description | Soil and Plant Analyzer Development (SPAD) value and leaf water content (LWC) are critical physiological parameters for agricultural irrigation and growth monitoring in late-maturing citrus. Accurate monitoring of citrus SPAD value and LWC is of great significance for guiding precision irrigation, improving water use efficiency, and enhancing yield. To rapidly and efficiently obtain the SPAD value and LWC of citrus orchards, this study extracted vegetation index (VI) and texture feature (TF) of late-maturing citrus at different growth stages based on UAV multi-spectral images. Feature variable selection methods (decision tree (DT) and least absolute shrinkage and selection operator (Lasso)) were combined with Support vector machine regression (SVR), AdaBoost (Ada), SVR-AdaBoost (SVR-Ada) and WOA-SVR-Ada. Models for estimating SPAD value and LWC in citrus orchards were constructed using VI, TF, and VI+TF as inputs. The results showed that the DT algorithm demonstrated superior capability in identifying feature variables compared to the Lasso. The integration of VI and TF can enhance the inversion accuracy of citrus SPAD value and LWC models. Compared to the SVR, Ada and SVR-Ada, the WOA-SVR-Ada model, constructed by combining the DT algorithm with VI+TF as inputs (WOA-SVR-AdaD3), exhibited the highest estimation accuracy for both SPAD value and LWC. Therefore, combining feature variable selection methods with ensemble learning algorithms, along with the fusion of multi-feature information from UAV multispectral, holds promise for providing precise and robust estimations of SPAD value and LWC for late-maturing citrus in the seasonal drought regions of Southwest China. |
| format | Article |
| id | doaj-art-7d5c756b9fc340a894002a468df6e03d |
| institution | OA Journals |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-7d5c756b9fc340a894002a468df6e03d2025-08-20T01:55:31ZengElsevierAgricultural Water Management1873-22832025-06-0131410952410.1016/j.agwat.2025.109524Inversion of citrus SPAD value and leaf water content by combining feature selection and ensemble learning algorithm using UAV remote sensing imagesQuanshan Liu0Fei Chen1Ningbo Cui2Zongjun Wu3Xiuliang Jin4Shidan Zhu5Shouzheng Jiang6Daozhi Gong7Shunsheng Zheng8Lu Zhao9Zhihui Wang10State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China; Correspondence to: College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China.State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China; Correspondence to: College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China.Soil and Plant Analyzer Development (SPAD) value and leaf water content (LWC) are critical physiological parameters for agricultural irrigation and growth monitoring in late-maturing citrus. Accurate monitoring of citrus SPAD value and LWC is of great significance for guiding precision irrigation, improving water use efficiency, and enhancing yield. To rapidly and efficiently obtain the SPAD value and LWC of citrus orchards, this study extracted vegetation index (VI) and texture feature (TF) of late-maturing citrus at different growth stages based on UAV multi-spectral images. Feature variable selection methods (decision tree (DT) and least absolute shrinkage and selection operator (Lasso)) were combined with Support vector machine regression (SVR), AdaBoost (Ada), SVR-AdaBoost (SVR-Ada) and WOA-SVR-Ada. Models for estimating SPAD value and LWC in citrus orchards were constructed using VI, TF, and VI+TF as inputs. The results showed that the DT algorithm demonstrated superior capability in identifying feature variables compared to the Lasso. The integration of VI and TF can enhance the inversion accuracy of citrus SPAD value and LWC models. Compared to the SVR, Ada and SVR-Ada, the WOA-SVR-Ada model, constructed by combining the DT algorithm with VI+TF as inputs (WOA-SVR-AdaD3), exhibited the highest estimation accuracy for both SPAD value and LWC. Therefore, combining feature variable selection methods with ensemble learning algorithms, along with the fusion of multi-feature information from UAV multispectral, holds promise for providing precise and robust estimations of SPAD value and LWC for late-maturing citrus in the seasonal drought regions of Southwest China.http://www.sciencedirect.com/science/article/pii/S0378377425002380UAV multispectralVegetation index (VI)Texture feature (TF)Drip irrigation citrus orchardMachine learning |
| spellingShingle | Quanshan Liu Fei Chen Ningbo Cui Zongjun Wu Xiuliang Jin Shidan Zhu Shouzheng Jiang Daozhi Gong Shunsheng Zheng Lu Zhao Zhihui Wang Inversion of citrus SPAD value and leaf water content by combining feature selection and ensemble learning algorithm using UAV remote sensing images Agricultural Water Management UAV multispectral Vegetation index (VI) Texture feature (TF) Drip irrigation citrus orchard Machine learning |
| title | Inversion of citrus SPAD value and leaf water content by combining feature selection and ensemble learning algorithm using UAV remote sensing images |
| title_full | Inversion of citrus SPAD value and leaf water content by combining feature selection and ensemble learning algorithm using UAV remote sensing images |
| title_fullStr | Inversion of citrus SPAD value and leaf water content by combining feature selection and ensemble learning algorithm using UAV remote sensing images |
| title_full_unstemmed | Inversion of citrus SPAD value and leaf water content by combining feature selection and ensemble learning algorithm using UAV remote sensing images |
| title_short | Inversion of citrus SPAD value and leaf water content by combining feature selection and ensemble learning algorithm using UAV remote sensing images |
| title_sort | inversion of citrus spad value and leaf water content by combining feature selection and ensemble learning algorithm using uav remote sensing images |
| topic | UAV multispectral Vegetation index (VI) Texture feature (TF) Drip irrigation citrus orchard Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S0378377425002380 |
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