Modeling and simulation of agriculture production system considering seasonal variable information using big data analysis

Abstract

The agricultural sector is required to produce a steady, year-round supply of crops to maintain the quality of life of consumers. The organization of agricultural production has, therefore, become increasingly important. This study aims to evaluate the agricultural production systems for storage shortages and quality loss while considering seasonal changes in crop growth. We first discuss the occurrence of storage shortages and quality loss. Next, we investigate the production and crop growth processes in fields used by an agricultural production corporation. We examined the statistical treatment of growth and the relationship between crop growth and the field environment using the data collected from field sensors. We then analyzed the data obtained in the observation. Based on the results of the analysis, we proposed a model for the work process in the field, which is a prediction algorithm for crop growth that considers seasonal changes. We then implemented the simulation model. We verified the accuracy of the proposed growth prediction algorithm through a case study and used the simulation to evaluate storage shortages and quality loss.