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Hyperspectral inversion model of ginkgo leaf yield prediction based on machine learning
Zheng Zuo1, Maocheng Zhao1, Liang Qi1,2
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Frontiers in Plant Science
|December 15, 2025
Summary
Airborne hyperspectral imaging offers a non-destructive method for estimating ginkgo leaf yield. This advanced technology improves accuracy and efficiency over traditional manual assessments, benefiting crop monitoring.
Area of Science:
- Agricultural Science
- Remote Sensing
- Plant Physiology
Background:
- Ginkgo biloba leaf yield is crucial for assessing plant health and growth.
- Traditional manual yield assessment methods are inefficient, labor-intensive, and costly.
Purpose of the Study:
- To develop an accurate, non-destructive, and efficient method for estimating ginkgo leaf yield using airborne hyperspectral imaging.
- To optimize hyperspectral data preprocessing and feature selection for yield prediction.
Main Methods:
- Airborne hyperspectral imaging was used to collect canopy spectral data.
- Various preprocessing techniques (MSC, SNV, SG, FD, SS) and feature selection algorithms (PSO, SPA, PCA, LASSO, CARS, PSAMA) were evaluated.
- Machine learning models including PLSR, RF, KNNR, LSTM, SVR, BiLSTM, and BiLSTM-GS were employed for yield prediction.
Main Results:
- The SNV-PLSR model showed good performance (Rp² = 0.7831).
- An optimized model combining SNV preprocessing, selected vegetation indices (SAVI, MSAVI, NDRE, SIPI), Region of Interest Pixels (ROP), and the BiLSTM-GS algorithm achieved high prediction accuracy (Rp² = 0.8795).
Conclusions:
- Airborne hyperspectral canopy-based estimation is a viable and accurate technology for monitoring ginkgo leaf yield.
- This non-destructive approach significantly improves upon traditional manual methods.
