Machine Learning and Hybrid Model Building Mechanism for Forecasting Agricultural Market Behaviour: A Case Study of Vegetable Crops

A strategic market intelligence mechanism is essential for stakeholders to make well-informed decisions on production, pricing, and marketing strategies; however, the complex behaviour of agricultural markets, coupled with the presence of non-stationarity, non-normality, and non-linearity in the arrivals and prices of perishable agricultural commodities pose challenges for traditional statistical models in modelling and forecasting. This study sought to overcome these challenges by employing machine learning and hybrid models for vegetable crops. Machine learning techniques, known for their flexibility with non-stationary, non-normal, and non-linear data, were showcased in the empirical illustration, highlighting their effectiveness. The trends of arrivals and prices were found to be significantly positive (p<0.01) for tomato and capsicum. Time delay neural network (TDNN) and hybrid models outperformed the seasonal ARIMA models based on lower RMSE and MAPE for the modelling and forecasting. Utilizing the best-fit models, the prices of tomato and capsicum were forecasted to be highest during June 2024 with ?2267 and 2623 per q. This study contributed to the growing body of applications of machine learning in agricultural systems. It will help develop a robust market intelligence mechanism to address uncertainties in agricultural markets.