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The forecasting behaviour of millet plays a critical role in production planning at the Indian farm level. This study made an effort to forecast the area and production of small millets in India with time series analysis. The performance of the forecasting models was appraised and collated by the Mean Absolute Percentage Error (MAPE), Partial Autocorrelation Function (PACF) and Auto Correlation Function (ACF) criteria. For this analysis, the yearly data of the area and production of small millet from 1950 to 2021 were calculated. Among all Autoregressive Integrated Moving Average (ARIMA) models, ARIMA (0,1,0) was found to be the best fitted for forecasting the area and production of minor millets in India since, principally, this model relies on historical ideals of the sequences in addition to earlier error relations for forecasting minor millets and it does not adopt information of any fundamental model or associations as in some other approaches. The predicted values of minor millet area showed decreased trend from 422.4 thousand hectares in the year 2022 to 409.2 thousand hectares in the year 2026. Likewise, the production under small millets declined from 393.5 thousand tons to 159.5 thousand tons for the corresponding period. Hence, production of these crops can be enhanced by suitable use of inputs and timely application of inputs, high yielding varieties, government interventions like policy support, subsidising through the Public Distribution System and awareness by the way of propaganda and demonstration.

The major objective of the present study was to explore if Artificial Neural Network (ANN) models with back propagation could efficiently predict the rice yield under various climatic conditions; ground-specific rainfall, ground-specific weather variables and historic yield data. The back propagation algorithm will calculate each expected weight using the error rate as the activity level of a unit was altered.  The errors in the model during the training phase were solved during the back-propagation. The paddy yield prediction took various parameters like rainfall, soil moisture, solar radiation, expected carbon, fertilizers, pesticides, and the long-time paddy yield recorded using Artificial Neural Networks. The R2 value on the test set was found to be 93% and it showed that the model was able to predict the paddy yield better for the given data set. The ANN model was tested with learning rates of 0.25 and 0.5. The number of hidden layers in the first layer was 50 and in the second hidden layer was 30. From this, the testing value of R square was 0.97. The observations with the ANN Model showed that i) the best result for the test set was  R2 value of 0.98, ii) the two hidden layers kept with 50 neurons in the first layer and 30 neurons in the second one, iii) the learning rate was of 0.25. With all these configurations, maximum yield is possible from the paddy crop.