Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India

Abstract

Background Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions. Methods In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India. Results An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models. Conclusions Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.

Hybrid approach for vegetable price forecasting in electronic commerce platform

“…Paul et al [18] carried out an empirical comparison of the predictive accuracies of different models (machine learning models) with the usual stochastic model for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India by means of model confidence set (MCS), and other accuracy measures such as mean error (ME), RMSE, mean absolute error (MAE), and mean absolute percentage error (MAPE). The best performer among the machine learning techniques is the general regression neural network (GRNN).…”

Influence of Integrated Nutrient Management on Growth, Flowering Behaviour and Yield of Brinjal (Solanum Melongena L.) Cv. Pusa Purple Round

“…Among the tropical vegetables growing most frequently in India is brinjal. The highly nutritious vegetable brinjal offers the following nutrients: 52.0 mg of chlorine, 47.0 mg of phosphorus, 44.0 mg of sulphur, 6.4 mg of vitamin A, 18.0 mg of calcium, 24 kcal of energy, 1.3 g of fiber, 0.9 mg of iron, 1.4 g of protein, 12.0 mg of vitamin C, and 18.0 mg of oxalic acid are all present in 100g of brinjal (Paul et al, 2022).…”

Evaluation of the behavior of prices and market arrivals of tomatoes – A case study of West Bengal, India

“…This will help to provide insights into the differences in price across regions since it indicates that not only do consumers pay different prices in different areas for the same products (unless subsidized by programs like PDS), but those producers also receive varied prices depending on their geographical location (Chatterjee & Kapur, 2016;Paul et al, 2022a). Access to price and arrival data also enhances farmers' bargaining power and fosters increased competition among traders by enabling informed decisions, allowing farmers to strategically navigate alternative nearby markets and secure favorable prices for their produce (Paul et al, 2022b). To formulate a good agricultural pricing strategy for price stabilization, a thorough understanding of the interrelationship between market arrivals and farm product prices is required.…”