Inversion of SPAD Values of Pear Leaves at Different Growth Stages Based on Machine Learning and Sentinel-2 Remote Sensing Data

Ning Yan, Qu Xie, Yasen Qin, Qi Wang, Sumin Lv, Xuedong Zhang () and Xu Li ()
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Ning Yan: College of Information Engineering, Tarim University, Alaer 843300, China
Qu Xie: College of Information Engineering, Tarim University, Alaer 843300, China
Yasen Qin: College of Information Engineering, Tarim University, Alaer 843300, China
Qi Wang: College of Information Engineering, Tarim University, Alaer 843300, China
Sumin Lv: College of Information Engineering, Tarim University, Alaer 843300, China
Xuedong Zhang: College of Information Engineering, Tarim University, Alaer 843300, China
Xu Li: College of Information Engineering, Tarim University, Alaer 843300, China

Agriculture, 2025, vol. 15, issue 12, 1-24

Abstract: Chlorophyll content is a critical indicator of the physiological status and fruit quality of pear trees, with Soil Plant Analysis Development (SPAD) values serving as an effective proxy due to their advantages in rapid and non-destructive acquisition. However, current remote sensing-based SPAD retrieval studies are primarily limited to single phenological stages or rely on a narrow set of input features, lacking systematic exploration of multi-temporal feature fusion and comparative model analysis. In this study, pear leaves were selected as the research object, and Sentinel-2 remote sensing data combined with in situ SPAD measurements were used to conduct a comprehensive retrieval study across multiple growth stages, including flowering, fruit-setting, fruit enlargement, and maturity. First, spectral reflectance and representative vegetation indices were extracted and subjected to Pearson correlation analysis to construct three input feature schemes. Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. The results demonstrated that (1) both spectral reflectance and vegetation indices exhibited significant correlations with SPAD values, indicating strong retrieval potential; (2) the OIA model consistently outperformed individual algorithms, achieving the highest accuracy when using the combined feature scheme; (3) among the phenological stages, the fruit-enlargement stage yielded the best retrieval performance, with R 2 values of 0.740 and 0.724 for the training and validation sets, respectively. This study establishes a robust SPAD retrieval framework that integrates multi-source features and multiple models, enhancing prediction accuracy across different growth stages and providing technical support for intelligent orchard monitoring and precision management.

Keywords: Sentinel-2; pear tree leaves; SPAD; machine learning; growth stages (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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