The Deep Learning Approach for Crop Selection and Yield Prediction using Bi-LSTM in Agriculture
Keywords:
Agriculture, Bidirectional Long Short-Term Memory, Crop Selection, Deep Learning, PredictionAbstract
Accurate crop yield prediction is essential for optimizing agricultural practices, ensuring food security, and maximizing resource efficiency. Traditional methods often fail to capture the complex, sequential dependencies in agricultural data, limiting their predictive accuracy. This work focuses on improving crop yield prediction by overcoming the drawbacks of conventional methods and integrating sequential data. The presented Bi-LSTM model provides better results than other machine learning and deep learning models since it uses all dependencies of temporal data of agriculture data. The study used Agricultural Crop Yield dataset then training and testing Bi-LSTM model. The performance is compared with other methods such as Linear Regression, Random Forest and basic LSTM to determine Mean Absolute Error, Root Mean Squared Error, R² score and Mean Absolute Percentage Error. The Bi- LSTM model yields the best result with MAE=0.32, RMSE =0.47 and R² Score =0.91. It efficiently incorporates features like rainfall, usage of fertilizers, which proves its applicability in the data of crop yields data. The analysis proves Bi-LSTM to be effective in predicting crop yield and offers a sound approach for decision support in agriculture.
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