Wheat Production Forecasting in Bangladesh Using Deep Learning Techniques

Abstract

Wheat is an important crop for Bangladesh, which is both a producer and a consumer of grain. Bangladesh's agricultural sector is vital to the country's economy and employment levels. Wheat is the most important winter crop that is grown during the winter (Rabi) season. Wheat is also an essential food staple. Wheat yield and production statistics in Bangladesh are typically released by the local crop reporting administration several months after harvest has taken place. The field data were gathered by hand from a predetermined list of villages to provide the basis for the statistical estimates. Forecasts of early season wheat production enable improved planning for wheat transactions on the global market, the maintenance of adequate stocks, the informing of policymaking, the setting of support prices, and an increase in market efficiency. The long-term viability of Bangladesh's wheat industry as well as its susceptibility to market swings is the topic that will be investigated in this study. According to the results of the experiment, Bangladesh's wheat crop area, production, and yield are all on the rise. The experiment was conducted in Bangladesh. In order to develop the model and obtain an estimate of the forecasting behavior, the ARIMA model methodology was utilized.