Application of modeling techniques for energy analysis of fruit production systems

Moving toward environmental sustainability in the fruit production systems needs accurate monitoring of the systems in terms of energy flow. In this regard, using regression models (i.e., Cobb–Douglas), Multiple Linear Regression (MLR)), and Artificial Neural Network (ANN), this paper examined the energy flow in the pomegranate orchards in Iran. Unlike other similar studies, in the current research, the cross-validation technique was employed to evaluate how choosing datasets would modify the behavior of the models. The results demonstrated that the total energy consumption and energy efficiency of the pomegranate production were 13,634.13 MJ ha−1 and 1.01, respectively. The results also indicated that among investigated MLR models, the pure-quadratic model had the best performance. Besides, among 13 evaluated ANN-training algorithms, the Bayesian regulation algorithm had the highest accuracy in prediction and the best model consisted of one hidden layer with three neurons (5–3–1 topology) with logarithm sigmoid activation function. The results highlighted that the ANN model (R2 = 0.91) outweighs the MLR model (R2 = 0.86). The results also approved that the proposed ANN model was capable to use different datasets with high generalizability. The results of sensitivity analysis claimed the significant role of water and fuel consumption and consequently, the importance of establishing management policies to optimizing the use of these inputs in the pomegranate orchards.