Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm
Achieving timely and non-destructive assessments of crop yields is a key challenge in the agricultural field, as it is important for optimizing field management measures and improving crop productivity. To accurately and quickly predict citrus yield, this study obtained multispectral images of citru...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/171 |
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| author | Wenhao Xu Xiaogang Liu Jianhua Dong Jiaqiao Tan Xulei Wang Xinle Wang Lifeng Wu |
| author_facet | Wenhao Xu Xiaogang Liu Jianhua Dong Jiaqiao Tan Xulei Wang Xinle Wang Lifeng Wu |
| author_sort | Wenhao Xu |
| collection | DOAJ |
| description | Achieving timely and non-destructive assessments of crop yields is a key challenge in the agricultural field, as it is important for optimizing field management measures and improving crop productivity. To accurately and quickly predict citrus yield, this study obtained multispectral images of citrus fruit maturity through an unmanned aerial vehicle (UAV) and extracted multispectral vegetation indices (VIs) and texture features (T) from the images as feature variables. Extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), gaussian process regression (GPR), and multiple stepwise regression (MSR) models were used to construct citrus fruit number and quality prediction models. The results show that, for fruit number prediction, the XGB model performed best under the combined input of VIs and T, with an R<sup>2</sup> = 0.792 and an RMSE = 462 fruits. However, for fruit quality prediction, the RF model performed best when only the VIs were used, with an R<sup>2</sup> = 0.787 and an RMSE = 20.0 kg. Although the model accuracy was acceptable, the number of input feature variables used was large. To further improve the model prediction performance, we explored a method that utilizes a hybrid coding particle swarm optimization algorithm (CPSO) coupled with XGB and SVM models. The coupled models had a significant improvement in predicting the number and quality of citrus fruits, especially the model of CPSO coupled with XGB (CPSO-XGB). The CPSO-XGB model had fewer input features and higher accuracy, with an R<sup>2</sup> > 0.85. Finally, the Shapley additive explanations (SHAP) method was used to reveal the importance of the normalized difference chlorophyll index (NDCI) and the red band mean feature (MEA_R) when constructing the prediction model. The results of this study provide an application reference and a theoretical basis for the research on UAV remote sensing in relation to citrus yield. |
| format | Article |
| id | doaj-art-e6cbe09cf33444feae7c1f5354372f9f |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-e6cbe09cf33444feae7c1f5354372f9f2025-01-24T13:17:01ZengMDPI AGAgronomy2073-43952025-01-0115117110.3390/agronomy15010171Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO AlgorithmWenhao Xu0Xiaogang Liu1Jianhua Dong2Jiaqiao Tan3Xulei Wang4Xinle Wang5Lifeng Wu6Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaSchool of Water Conservancy and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, ChinaFaculty of Foreign Languages and Cultures, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaAchieving timely and non-destructive assessments of crop yields is a key challenge in the agricultural field, as it is important for optimizing field management measures and improving crop productivity. To accurately and quickly predict citrus yield, this study obtained multispectral images of citrus fruit maturity through an unmanned aerial vehicle (UAV) and extracted multispectral vegetation indices (VIs) and texture features (T) from the images as feature variables. Extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), gaussian process regression (GPR), and multiple stepwise regression (MSR) models were used to construct citrus fruit number and quality prediction models. The results show that, for fruit number prediction, the XGB model performed best under the combined input of VIs and T, with an R<sup>2</sup> = 0.792 and an RMSE = 462 fruits. However, for fruit quality prediction, the RF model performed best when only the VIs were used, with an R<sup>2</sup> = 0.787 and an RMSE = 20.0 kg. Although the model accuracy was acceptable, the number of input feature variables used was large. To further improve the model prediction performance, we explored a method that utilizes a hybrid coding particle swarm optimization algorithm (CPSO) coupled with XGB and SVM models. The coupled models had a significant improvement in predicting the number and quality of citrus fruits, especially the model of CPSO coupled with XGB (CPSO-XGB). The CPSO-XGB model had fewer input features and higher accuracy, with an R<sup>2</sup> > 0.85. Finally, the Shapley additive explanations (SHAP) method was used to reveal the importance of the normalized difference chlorophyll index (NDCI) and the red band mean feature (MEA_R) when constructing the prediction model. The results of this study provide an application reference and a theoretical basis for the research on UAV remote sensing in relation to citrus yield.https://www.mdpi.com/2073-4395/15/1/171XGBoostSHAP analysismultispectraltexture featuresmachine learninghybrid coding |
| spellingShingle | Wenhao Xu Xiaogang Liu Jianhua Dong Jiaqiao Tan Xulei Wang Xinle Wang Lifeng Wu Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm Agronomy XGBoost SHAP analysis multispectral texture features machine learning hybrid coding |
| title | Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm |
| title_full | Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm |
| title_fullStr | Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm |
| title_full_unstemmed | Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm |
| title_short | Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm |
| title_sort | improvement of citrus yield prediction using uav multispectral images and the cpso algorithm |
| topic | XGBoost SHAP analysis multispectral texture features machine learning hybrid coding |
| url | https://www.mdpi.com/2073-4395/15/1/171 |
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