Hybrid Deep Learning Implementation for Crop Yield Prediction

Agriculture producers should be supported technologically in order to continue production a way that meets the worldwide food supply and demand. Automatic realization of crop yield estimation calculation is desired need farmers. also facilitates work agricultural with different goals such as imports exports. To achieve stated objectives, deep learning models have been developed estimated using parameters amount water per hectare, average sunlight received by fertilization number pesticides used area cultivation. With hybrid model created combining strengths LSTM CNN within scope this article, success rate data prediction has increased fine adjustments. Success rates 89.71 R2, 0.0035 MSE, 0.0248 RMSE, 0.0461 MAE, 10.10 MAPE achieved Proposed model. This competitive similar studies values.