PROSAIL-DNN: a fine-tuning transfer learning framework for field-scale oat leaf area index monitoring from UAV imagery

• A hybrid approach combining pre-training and fine-tuning is proposed for monitoring oat LAI from UAV imagery. • PROSAIL-DNN outperforms non-fine-tuning methods in estimating oat LAI across different growth stages. • PROSAIL-DNN maintains high accuracy with limited data, making it suitable for real-world agricultural applications. Accurate and efficient estimations of leaf area index (LAI) are crucial for crop management, including intelligent crop breeding, nutrient management, and yield prediction. Unmanned aerial vehicle (UAV)-based multispectral sensors with machine learning models provide high-precision solutions for LAI estimation but are hindered by the challenge of acquiring adequate ground truth data. Transfer learning, one type of deep learning framework, offers a solution by leveraging prior knowledge learned through models that have been pre-trained, thus ensuring robust performance despite limited data availability. This study evaluates the efficacy of the fine-tuning PROSAIL-Informed Deep Neural Network model (PROSAIL-DNN) for estimating LAI of different oat varieties and growth periods using UAV multispectral images. We compared this model with several widely used machine learning algorithms, including partial least squares (PLS), least absolute shrinkage and selection operator (Lasso), support vector regression (SVR), and extreme gradient boosting (XGBoost)), and a DNN, using training datasets consisting of 70 %, 60 %, and 50 % of the field data to assess the impact of data volume on model performance. Our results demonstrated that the PROSAIL-DNN model outperformed other algorithms across different growth periods and all growth periods. Specifically, with only 50 % of the training data used, the average R 2 of the PROSAIL-DNN model was 5.44 %, 28.76 %, 12.98 %, 12.72 %, and 22.90 % higher than those of DNN, Lasso, PLS, SVR, and XGBoost, respectively. The PROSAIL-DNN model also demonstrated higher accuracy in monitoring LAI during different growth periods, especially at early jointing period (P1) (R 2 = 0.838, RMSE = 0.376, and RPD = 2.483) and at post-heading period (P3) (R 2 = 0.881, RMSE = 0.298, and RPD = 2.896). Our findings underscore the potential of combining PROSAIL with deep transfer learning to accurately and robustly estimate oat LAI at various growth periods using UAV multispectral images with limited field data. This approach provides a solid foundation for applying transfer learning in crop monitoring and can be adapted for other crop variables in future studies.