Mushroom Demand Prediction Using Machine Learning Algorithms

With the expansion of the global mushroom industries, the prediction of future market demand and the production data is important for the further sales of mushroom. The mushroom industries usually receive dynamic demands which are highly non-seasonal and non-periodic in nature. As a result, it is a challenging task to ascertain future mushroom demand and production optimally. In a traditional approach, people produce a certain amount of mushroom in every season based on previous experiences that do not reflect the actual market demand. Therefore, in the case of the shortage of supply, most of the mushroom farms import the products from nearby farms or abroad. Alternatively, the surplus of products than demand is sold to the market at a cheaper rate before the products perish.This paper proposes a machine learning-based solution for the dynamic demand problem in mushroom farms. We have summarized the results obtained from three different machine learning models that are trained with the actual demands of the previous year's mushroom data. After that, we compare the test results given by each of the models to predict the future demand of mushrooms.