A Multilingual and State-Wise Crop Yield Prediction System Using Advanced Machine Learning Models

REGAMANDA LAKSHMAN SAI; PUSALA VENKATA SAIKIRAN; A John Clement Sundar

A Multilingual and State-Wise Crop Yield Prediction System Using Advanced Machine Learning Models

Author(s)	Mr. REGAMANDA LAKSHMAN SAI, Mr. PUSALA VENKATA SAIKIRAN, A John Clement Sundar
Country	India
Abstract	Abstract — Agriculture plays a vital role in the economic development of many countries, especially in India where crop production is highly dependent on environmental and regional factors. Accurate crop yield prediction is important for improving agricultural planning, resource management, and decision-making for farmers and policymakers. This paper proposes a Multilingual and State-Wise Crop Yield Prediction System using Advanced Machine Learning Models to estimate crop production based on agricultural data. The system utilizes machine learning algorithms such as Random Forest Regressor and XGBoost to analyze historical crop data and generate accurate yield predictions. Data preprocessing and analysis are performed using libraries such as Pandas and NumPy, while Scikit-learn is used for implementing machine learning models. The application is developed using Python as the core programming language. Furthermore, the proposed system incorporates multilingual support to enable farmers from different linguistic backgrounds to interact with the application in their preferred language, thereby improving accessibility and usability. The model is trained on state-wise agricultural datasets, allowing it to capture regional variations in climate, soil conditions, and crop patterns, which enhances prediction accuracy. Feature selection techniques are applied to identify the most influential factors affecting crop yield, such as rainfall, temperature, soil type, and fertilizer usage. In addition, the system includes data visualization tools to present insights in a clear and understandable manner, helping users interpret prediction results effectively. Performance evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to assess the accuracy of the models. The experimental results demonstrate that the proposed system provides reliable and efficient crop yield predictions, outperforming traditional statistical methods. Overall, this system aims to support smart agriculture by enabling data-driven decision-making, improving productivity, and reducing risks associated with uncertain environmental conditions.
Keywords	Machine Learning Advanced Machine Learning Models Random Forest Regressor XGBoost Predictive Analytics
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-03

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A Multilingual and State-Wise Crop Yield Prediction System Using Advanced Machine Learning Models

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