Abstract:
In Bangladesh's agricultural sector, dairy farming is vital since it greatly improves rural livelihoods, employment, and nutrition. The demand for dairy products including milk, butter, cheese, and yogurt has been rising gradually due to factors like population increase, urbanization, and shifting dietary preferences. Price volatility, which is caused by a number of economic and environmental reasons such as seasonal variations, feed prices, labor shortages, and erratic weather occurrences like droughts and floods, presents significant issues for the industry. Both producers and consumers experience uncertainty as a result of these price swings, which has an impact on financial stability and decision-making.The study investigates the use of Artificial Neural Networks (ANNs), a potent machine learning method modeled after the neural architecture of the human brain, to predict dairy product prices in Bangladesh in order to address this problem.ANNs are a perfect tool for forecasting price trends in the face of shifting environmental and economic conditions because they excel at examining intricate patterns and relationships in big datasets. ANNs can provide accurate forecasts that assist producers, consumers, and policymakers along the dairy value chain in making better decisions by using historical data on milk production, feed costs, seasonal trends, labor availability, and weather.This study examines how ANNs can estimate milking by-product prices and lessen the effects of price volatility in Bangladesh's dairy industry using time-series forecasting approaches. The results show that ANNs can accurately anticipate production, pricing, and resource allocation by capturing the complex interactions between many factors impacting dairy prices choices.The dairy business can become more robust and sustainable if ANN-based forecasting models are successfully implemented. This will raise efficiency, improve risk management, and increase price stability. This study provides important insights for the wider use of machine learning in agriculture and demonstrates how ANNs can improve decision-making in agricultural forecasting.
