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Inversion of nitrogen content in apple leaves using an explainable PSO-CNN model
Yuyang Ma1, Xicun Zhu1,2, Meixuan Li1
1College of Resources and Environment, Shandong Agricultural University, Tai'an, China.
Frontiers in Plant Science
|May 7, 2026
Summary
Hyperspectral technology combined with machine learning accurately estimates nitrogen in apple leaves. The particle swarm optimization-convolutional neural network (PSO-CNN) model shows superior performance for rapid nutrient diagnosis.
Area of Science:
- Agricultural remote sensing
- Plant physiology
- Machine learning applications
Background:
- Accurate nitrogen estimation is crucial for apple tree health and yield.
- Non-destructive methods for nutrient diagnosis are highly desirable in precision agriculture.
- Hyperspectral technology offers potential for detailed plant analysis.
Purpose of the Study:
- To develop and compare machine learning models for non-destructively estimating nitrogen content in Red Fuji apple leaves.
- To evaluate the performance of different algorithms, including Random Forest, Support Vector Machine, Convolutional Neural Network, and Particle Swarm Optimization-Convolutional Neural Network.
- To assess the model's generalization capability across different phenological stages.
Main Methods:
- Hyperspectral data were collected from apple orchards at two key growth stages.
- Characteristic wavelengths were selected after hyperspectral data preprocessing.
- Regression models (RF, SVM, CNN, PSO-CNN) were developed and compared for nitrogen content prediction.
- SHAP analysis was used to interpret model predictions.
Main Results:
- The Particle Swarm Optimization-Convolutional Neural Network (PSO-CNN) model significantly outperformed RF and SVM, achieving an R² of 0.774 on the test set.
- The PSO-CNN model demonstrated good generalization capability with acceptable prediction accuracy (R² = 0.625) across different phenological stages.
- SHAP analysis indicated that the model utilizes near-infrared and short-wave infrared bands, correlating with leaf biochemical characteristics.
Conclusions:
- The PSO-CNN model provides a robust and accurate method for non-destructive nitrogen estimation in apple leaves.
- This approach enables rapid and precise nutrient diagnosis, supporting optimized orchard management.
- The study highlights the effectiveness of integrating hyperspectral imaging with advanced deep learning for plant nutrient monitoring.
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