Optimization of Irrigation Through Data-Driven Approaches in Precision Agriculture Utilizing Deep Neural Networks
Dr. Nikhat Akhtar
Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow
Sarita Maurya
Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow
Sunny Kumar
Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Neha Anand
Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Dr. Yusuf Perwej
Professor, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
📌 DOI: https://doi.org/10.63920/tjths.52012
🔑 Keywords: Agriculture Data, Feature Selection, Long Short-Term Memory, Crop Selection, Pre-Process, Crop Yield Prediction.
📅 Publication Date: 06 April 2026
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Abstract:
Agriculture is an essential vocation worldwide, dependent on climate conditions and rainfall. This article aims to use climatic, soil, and temperature data to forecast crop yield in advance. This research presents a classification-oriented methodology for forecasting agricultural output using Long Short-Term Memory (LSTM) integrated with an Attention Mechanism. The Government of Karnataka's Economics and Statistics department gather manual data. This method used data from the Department of Economics and Statistics about three crops: jowar, rice, and ragi. The linear interpolation method is used to address the missing and null values in the dataset. The feature selection process is advantageous for the Correlation-based Feature Selection Algorithm (CBFA) and the Variance Inflation Factor Algorithm (VIF) since it facilitates the identification and elimination of correlated feature groups. We use Accuracy, R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to evaluate the model's performance. The proposed LSTM model yields assessment metrics with accuracy, R2, MAE, MSE, and RMSE values of around 99.10%, 0.44, 0.132, and 0.233, respectively.
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📖 How to Cite
Dr. Nikhat A., Sarita M., Sunny K., Neha A., Dr. Yusuf P. (2026). Optimization of Irrigation Through Data-Driven Approaches in Precision Agriculture Utilizing Deep Neural Networks. TEJAS J. Technol. Humanit. Sci.,, Vol. 05, Issue 02. https://doi.org/10.63920/tjths.52012
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