Individual sapling-level leaf chlorophyll content estimation in Ginkgo biloba with weed background: A novel deep-learning approach coupling APUNet-YOLO with PROSAIL-DNN

• Novel APUNet model enhanced the precision of sapling crown extraction. • Hyperparameter-optimized YOLO boosted accuracy in individual sapling detection. • Optimized PROSAIL-DNN model improved performance in chlorophyll estimation. • APUNet-YOLO overcomes weed interference to enable highly accurate estimation. • Coupled models enabled chlorophyll mapping in complex weed backgrounds. Accurate estimation of chlorophyll content helps understand the physiological health of saplings. Traditional field measurement methods limit large-scale applications, while remote sensing monitoring methods, though improving efficiency, still face challenges in data analysis and processing. A key challenge is extracting the canopy of individual trees and estimating chlorophyll content amid interference from weed vegetation. To overcome this challenge, this study develops a novel coupled deep learning approach. First, an Attention and Pyramid U-Net (APUNet) was proposed to improve the segmentation accuracy of Ginkgo seedlings crown boundaries in multispectral drone images. Second, the hyper-parameter combination of the You Only Look Once (YOLO) model was optimized to improve sapling detection accuracy under complex conditions with high weed coverage. Next, a sliding window method was developed to reduce duplicate detection rates for detecting all saplings in the entire image, and the intersection of APUNet and detection results was used for segmentation of individual saplings. Finally, a PROSAIL-Deep Neural Network (DNN) model for chlorophyll estimation was developed, and the impact of using APUNet YOLO to remove weed pixels on chlorophyll estimation was investigated, along with how over-coverage and under-coverage during the segmentation process affect the accuracy of chlorophyll estimation. The results show that, compared to U-Net, APUNet improved segmentation performance, with the IoU increasing from 0.697 to 0.736. After hyper-parameter optimization through grid search, the YOLOv8l model achieved an mAP50 of 0.968 and when this model was applied to full-image detection using a sliding window approach, its duplicate detection rate (ddr) decreased by 11.2% compared with the general method. In chlorophyll inversion, the chlorophyll estimation accuracy reached its highest after using APUNet-YOLO to reduce the interference from weed pixels, with an R 2 of 0.752 and an RMSE of 7.143 μg/cm 2 . Meanwhile, the sensitivity analysis of the impact of segmentation errors on chlorophyll estimation accuracy shows that, compared to under-coverage segmentation, over-inclusion segmentation leads to a greater decline in chlorophyll estimation accuracy. This novel coupled model can effectively monitor the chlorophyll content of Ginkgo saplings in settings with complex backgrounds, providing a practical possibility for the realization of precision forestry.