Modeling of greenhouse agro-ecosystem using optimally designed bootstrapping artificial neural network

Abstract

The greenhouse environment is a complex multi-scale integrated nonlinear system. This agro-ecosystem is composed of crop and greenhouse climate which are based on the existence of two different timescales. The effect of temperature plays a vital role in the variation of phenotypic data in crop growth. Thus, two different models, one for capturing the climate dynamics inside the greenhouse and other for crop dynamics, are essential. First, the neural network is used to predict the inside environment, given the outside conditions and the operation of the control equipment. The inputs of the network are meteorological parameters, whose measurements are costly and time consuming to acquire. So, instead of measuring all the parameters used in the physical modeling, the most significant relevant input parameters which give same modeling efficiency are identified. To avoid overfitting of the data and to realize the best prediction results with the simplest structure, an enhanced pruning algorithm is implemented for topology optimization of the artificial neural network. The pruning algorithm is discussed and exemplified via simulations. By plotting the training error, test error, and final prediction error (FPE) estimates over the course of pruning the network weights, it is inferred that a network with minimum of 15 weights is reliable to model greenhouse environment dynamics. With the Optimal Brain Surgeon (OBS) algorithm, a reduction of the number of weights from 141 to 76 (46%) in the first step and finally to 15 (89%) was achieved and the percentage prediction error is reduced from 13.13% for the complete network structure to 4.35% for final pruned network. Secondly, in order to study the progress of crop ontogeny, bootstrap resampling-based artificial neural network is developed with limited destructive measurements. The notion of prediction performance and the efficiency of the bootstrapped crop phenotypic neural network model are evaluated by root mean square error (RMSE), mean square error (MSE) and Nash and Sutcliffe efficiency (NSE) criteria. The net assimilation rate which determines the growth rate of the plant inside the greenhouse environment is calculated. The resulting model can be used for growth assessment, understanding crop physiology and yield prediction.