Effects of transfer learning on seedling detection from drone imagery: a layer freezing study with faster R-CNN

Effective decision-making in forest management relies on accurate and timely data, yet conventional manpower-based methods often fail to provide sufficient coverage. This study develops a cost-effective, deep learning-based monitoring technique for assessing replanted areas following timber harvesting, utilizing a small, field-verified dataset. Orthophotos of a two-year-old Larix kaempferi plantation were acquired using drones, and seedling locations were identified through field surveys. The Faster R-CNN model with a ResNet-50 backbone was trained under different layer freezing settings and with or without pretrained weights. To improve experimental accuracy, data augmentation, k-fold cross-validation, and hyperparameter random search were applied. Freezing some of the initial layers while fine-tuning the remaining layers with pretrained weights resulted in the best performance, achieving an F1 score of 0.87-surpassing the 0.84 score obtained without pretrained weights. These findings demonstrate that transfer learning and layer-freezing strategies effectively enhance seedling detection performance, even with limited field validation data. The proposed method offers a cost-effective approach to forest management data analysis by reducing reliance on traditional survey methods while simultaneously improving data collection efficiency and utilization.