A Machine Learning Approach for Predicting Crop Yield based on Meteorological Data and Satellite Imagery
Keywords:
Crop yield prediction, machine learning, meteorological data, satellite imagery, remote sensing, Random Forest, Support Vector Machines, deep learning, vegetation index, precision agriculture, weather forecasting, spatial data, agricultural optimization, crop health, predictive modeling.Abstract
Accurate crop yield prediction is essential for ensuring food security and optimizing agricultural practices. This paper presents a machine learning approach for predicting crop yield by integrating meteorological data and satellite imagery. By utilizing machine learning algorithms, such as Random Forest, Support Vector Machines (SVM), and deep learning techniques, we model the complex relationships between environmental factors, including temperature, rainfall, and soil moisture, with crop yield outcomes. Satellite imagery, specifically multispectral and hyperspectral data, provides additional spatial information related to crop health, vegetation index, and soil conditions. These features are extracted from remote sensing images to enhance the model's predictive capability. The combination of meteorological data and satellite imagery allows for a more comprehensive understanding of the environmental influences on crop production. The proposed method is evaluated on multiple datasets from different regions and crop types, with performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to assess accuracy. The results demonstrate the effectiveness of the model in providing timely and accurate yield forecasts, thereby supporting decision-making in agriculture. This approach shows potential for enhancing precision farming, improving resource management, and optimizing crop production at a global scale.
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