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.

Bundelkhand region contributes more than half of total pulse area of the Uttar Pradesh state but the productivity is below the state average, which calls for various technological interventions, development of infrastructure and marketing strategies. This study assessed the profitability of pulse cultivation, identified the constraints and suggested policy measures using the data collected during 2016-2017 from 100 pulse growers selected from two backward districts of Bundelkhand region, namely Jalaun and Hamirpur. Growth in area, production and yield was estimated using data for 1980-2015 through compound annual growth rate and the highest growth was observed during 1980-1990 period. Modern cost concepts were used to assess the profitability of pulse cultivation and results revealed that the cost of cultivation per hectare was significantly higher in pigeon pea in comparison to gram, pea and lentil crops. The marketing charges paid by the village trader, wholesaler and retailer ranged between INR 20 to INR 40 per quintal for different crops. It was also observed that the quantum of marketable surplus and its percentage share to total production in pigeon pea, gram and lentil increased with the increase in size of land holding. The pulse production in the region faced with constraints related to production, processing and marketing. Hence, technologies and infrastructure need to be embraced through suitable policies to favour farmers, so as to maintain balance and keep the interest of both producers and the consumers.