Optimal Strategy for Designing a Multitask Learning-Based Hybrid Model to Predict Wheat Leaf Nitrogen Content

By combining a multitask deep learning method and a nitrogen PROSPECT and scattering by arbitrarily inclined leaves (N-PROSAIL) model, we proposed a multitask learning-based hybrid model (ML-HM) for leaf nitrogen content (LNC) prediction in a previous study. To provide an optimal ML-HM design for LNC prediction, this study focused on analyzing how factors, such as the simulated data distribution and sample size and the simulated and measured data batch sizes, affect the ML-HM accuracy. For this purpose, different scenarios for the above three factors were generated. ML-HMs were designed under these scenarios, and the performance was evaluated. The results showed that the simulated data distribution affects the ML-HM inversion accuracy, and it is better to use a priori knowledge to set the range and sampling strategy for the N-PROSAIL input variables to obtain a generated simulated data distribution that is similar to that of the measured data. The ML-HM accuracy increases with increasing measured sample size, but it does not change in an obvious manner once a certain threshold is reached. Thus, it is better to apply the sample size determination method based on simple random sampling to calculate the required sample size. The simulated and measured data batch sizes significantly affect the ML-HM accuracy, and we recommended creating a model for ML-HM accuracy prediction based on a certain number of batch size scenarios and using it to estimate suitable batch sizes of simulated and measured data to design an ML-HM.