| Application of machine learning algorithms in estimating chlorophyll in apples |
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| DOI：10.7606/j.issn.1000-7601.2026.03.26 |
| Key Words: apple chlorophyll multispectral machine learning |
| Author Name | Affiliation | | GONG Zhi | College of Hydraulic and Architectural Engineering, Tarim University, Alar, Xinjiang 843300, China | | LIU Jiangfan | College of Hydraulic and Architectural Engineering, Tarim University, Alar, Xinjiang 843300, China | | GUO Xinhua | College of Hydraulic and Architectural Engineering, Tarim University, Alar, Xinjiang 843300, China | | SHAO Jingcui | College of Hydraulic and Architectural Engineering, Tarim University, Alar, Xinjiang 843300, China | | LI Zhaoyang | College of Hydraulic and Architectural Engineering, Tarim University, Alar, Xinjiang 843300, China
Western Ｒesearch Institute, CAAS, Changji, Xinjiang 831100, China
Key Laboratory of Northwest Oasis Water\|Saving Agriculture, Ministry of Agriculture and Ｒural Affairs, Shihezi, Xinjiang 832000, China |
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| Abstract: |
| To explore the applicability of machine learning algorithms in estimating apple canopy chlorophyll content in the Southern Xinjiang region and construct an optimal chlorophyll estimation model covering the entire growth period of apples, this study used mature apple trees as subjects, collected UAV multispectral imagery and canopy chlorophyll content data at various growth stages, calculated multiple vegetation indices, and constructed estimation models in combination with measured chlorophyll content. The study systematically compared the estimation accuracy of Partial Least Squares Regression (PLSR), Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Least Absolute Shrinkage and Selection Operator regression (LASSO), Ridge Regression (RR), Neural Network (NN), and Stepwise Regression (SR) to screen the optimal model for each growth stage and the best model covering the entire growth period. The results showed that: (1) Vegetation indices exhibited good correlation with chlorophyll content, with correlation coefficients between NDVI, OSAVI, RVI, SAVI, and chlorophyll content all exceeding 0.6. (2) During the young fruit stage, the RF model achieved the highest accuracy (R2=0.626, RMSE=0.129); during the fruit expansion stage, the XGBoost model performed best (R2=0.720, RMSE=0.144); during the fruit coloring stage, the RF model achieved the highest accuracy (R2=0.667, RPD=1.733). (3) Although the RF and XGBoost models showed comparable accuracy during the fruit expansion stage, the RF model demonstrated stable and high accuracy throughout the entire growth period and was therefore identified as the optimal chlorophyll estimation model covering the entire growth period of apples. |
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