<p>Agriculture plays a crucial role in the Indian economy because in India, nearly 58% of people are dependent on agriculture for their livelihood. A recent survey by FAO (Food and Agricultural Organization) shows every year, nearly agricultural production increased by 4.1% in the last ten years. This organization analyzed that due to the pandemic situation, the demand for agricultural products is getting slowed down because of price fluctuations. The main problem faced by the farmers, government, and policymakers is fixing prices for every crop cultivated in India. Because the price range of the agricultural product fully depends on the cultivation of the particular crop in a certain period or season. Some traditional techniques were followed to predict the price of a crop, but it consumes a significant amount of time, and it cannot process the temporal data. To overcome this issue, ML and DL-based algorithms are used in the agricultural field. The first phase of the research predicted the tomato price and the current phase implements the different types of crop price prediction using a focused attention mechanism to predict the crop price cultivated all over India. The multivariate agriculture price dataset is collected from the Kaggle website. The proposed model integrated the GRU and BiGRU with an attention mechanism to reduce the error value and optimize the performance of the model. The best performance is attained by the proposed BiGRU-focused attention mechanism with less error value of MAE, MSE, RMSE, and R-square. Moreover, the proposed result of performance metrics is compared with the existing model.</p>