Net Photosynthesis Prediction by Deep Learning for Commercial Greenhouse Production

The amount of net photosynthesis of leaves is a significate factor for the growth of plants. Therefore, monitoring the real-time net photosynthesis plays an essential role in improving the quality of productions in commercial greenhouses. Net photosynthesis mainly depends on three environmental parameters, that are light level, temperature and CO2 concentration. However, it is challenging to calculate accurate net photosynthesis due to the highly nonlinear relation. In this paper, Deep Learning (DL) is utilized to model this relationship in order to predict the net photosynthesis based on the three inputs. Firstly, the architecture of a Deep Neural Network (DNN) model is designed according to the features of this problem, and three activation functions are concerned for the DNN model design. Secondly, a training dataset is established, and two schedules of Learning Rate (LR), fixed LR and exponential decay LR, are elaborated. Then, to select the optimal hyperparameters for the DNN model, experiments of hyperparameters tuning related to activation functions and LR schedules are implemented, respectively. Finally, through a comprehensive evaluation of the training speed and the prediction accuracy, a DNN model that is with ReLU activation function and decay LR is determined. This DNN model can perform a dramatically high prediction accuracy in a fast training convergence speed for solving the proposed net photosynthesis prediction problem.