Mirchi Crop Yield Prediction based on Soil and Environmental Characteristics using modified RNN

The economy may be significantly impacted by agriculture. Economic development is impacted by both the direct and indirect GDP (gross domestic product) contributions that agriculture makes. Regular analysis of soil and weather monitoring is essential for ensuring crops develop. In the past, most research on yield prediction based on a variety of variables, including the environment and kind of soil, was done right before planting. However, due to the frequent variation in soil and environmental characteristics, we must regularly understand the soil's features and external factors and take the necessary actions. Inorder to forecast the most effective combinations of soil and environmental parameters, a deep learning-based modified RNN model is employed in this work. The proposed performs better in the forecasting of soil and environmental parameters of Mirchi yield more precisely than the state-of-art models.