Application of Deep Neural Network on Net Photosynthesis Modeling

Abstract

Photosynthesis is a crucial biochemical process for plant growth, which is determined by multiple environmental factors and other organic matter. In the horticultural industry, the environmental conditions in commercial greenhouses directly impact the quality of productions. Predicting the Net Photosynthesis (Pn) of plants based on the environmental parameters can help growers optimize the climate in greenhouse systems, thereby ensuring the quality of production. Meanwhile, due to the greenhouse climate can be controlled according to the prediction results, excess energy consumption can be avoided, so the production cost can be reduced. However, since the photosynthesis reaction is a highly nonlinear biochemical process, it is difficult for traditional algorithms to describe the hidden effects of individual elements. In previous related works, polynomial fitting was utilized for modeling the relation between Pn and environmental elements. In this paper, a Deep Learning (DL) method is explored to predict the Pn based on three inputs: light level, CO2 concentration and temperature. An exponential decay learning rate is applied in the training process to ensure convergence performance while increasing the convergence speed. Then, the performance of various Deep Neural Network (DNN) architectures is experimented and compared within this modeling problem. Finally, through a comprehensive analysis of the accuracy of individual architecture, a particular architecture is determined to solve this problem. According to the test results, the selected DNN can successfully predict the Pn based on the three environmental elements with high accuracy.