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Horticulturae
Volume 11
Issue 10
10.3390/horticulturae11101223
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Article

UAV Multispectral Data Combined with the PROSAIL Model Using the Adjusted Average Leaf Angle for the Prediction of Canopy Chlorophyll Content in Citrus Fruit Trees

by
Shiqing Dou
1,*,
Yichang Hou
1,
Rongbin Wang
2,
Minglan Li
1,
Shixin Yuan
1,
Zhengmin Mei
3,
Yaqin Song
3 and
Jichi Yan
4
1
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
2
Shandong Mingjia Survey and Surveying Co., Ltd., Zibo 255086, China
3
Guangxi Academy of Specialty Crops, Guilin 541004, China
4
College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, China
*
Author to whom correspondence should be addressed.
Horticulturae 2025, 11(10), 1223; https://doi.org/10.3390/horticulturae11101223
Submission received: 9 September 2025 / Revised: 2 October 2025 / Accepted: 10 October 2025 / Published: 11 October 2025
(This article belongs to the Section Fruit Production Systems)
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Abstract

Canopy chlorophyll content (CCC) is an important index for monitoring the growth and estimating the productivity of citrus fruit trees. This study optimized the PROSAIL model by adjusting the average leaf angle (ALA) parameter. A hybrid inversion model was then developed by combining the simulated data with UAV multispectral measurements using machine learning to determine the optimal data fusion ratio for improved citrus CCC prediction. The results show that (1) the most pragmatic accommodation for the hybrid inversion model in this study is the 1:4 ratio of measured data to simulated data; (2) the adjusted ALA (ALAadj) value of citrus fruit trees is 42°, and the spectral response region of the adjusted PROSAIL parameters is more conducive to leaf chlorophyll content (LCC) and the leaf area index (LAI) for CCC modeling; and (3) the ALAadj hybrid inversion model showed significantly better performance than the ALA-unadjusted model under all four machine learning methods, with the peak prediction accuracy, measured by R2, rising from 0.723 to 0.823—a 13.8% increase. The proposed method effectively improves the prediction accuracy of citrus CCCs, demonstrating the strong potential of the ALAadj-based PROSAIL model for UAV-scale CCC monitoring.
Keywords: citrus canopy chlorophyll; average leaf angle; PROSAIL; multispectral UAV; machine learning citrus canopy chlorophyll; average leaf angle; PROSAIL; multispectral UAV; machine learning

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MDPI and ACS Style

Dou, S.; Hou, Y.; Wang, R.; Li, M.; Yuan, S.; Mei, Z.; Song, Y.; Yan, J. UAV Multispectral Data Combined with the PROSAIL Model Using the Adjusted Average Leaf Angle for the Prediction of Canopy Chlorophyll Content in Citrus Fruit Trees. Horticulturae 2025, 11, 1223. https://doi.org/10.3390/horticulturae11101223

AMA Style

Dou S, Hou Y, Wang R, Li M, Yuan S, Mei Z, Song Y, Yan J. UAV Multispectral Data Combined with the PROSAIL Model Using the Adjusted Average Leaf Angle for the Prediction of Canopy Chlorophyll Content in Citrus Fruit Trees. Horticulturae. 2025; 11(10):1223. https://doi.org/10.3390/horticulturae11101223

Chicago/Turabian Style

Dou, Shiqing, Yichang Hou, Rongbin Wang, Minglan Li, Shixin Yuan, Zhengmin Mei, Yaqin Song, and Jichi Yan. 2025. "UAV Multispectral Data Combined with the PROSAIL Model Using the Adjusted Average Leaf Angle for the Prediction of Canopy Chlorophyll Content in Citrus Fruit Trees" Horticulturae 11, no. 10: 1223. https://doi.org/10.3390/horticulturae11101223

APA Style

Dou, S., Hou, Y., Wang, R., Li, M., Yuan, S., Mei, Z., Song, Y., & Yan, J. (2025). UAV Multispectral Data Combined with the PROSAIL Model Using the Adjusted Average Leaf Angle for the Prediction of Canopy Chlorophyll Content in Citrus Fruit Trees. Horticulturae, 11(10), 1223. https://doi.org/10.3390/horticulturae11101223

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