AI-Powered Crop Yield Prediction Using Multimodal Data Fusion

Manaswini Davuluri

Abstract


Accurate crop yield prediction is essential for ensuring food security and optimizing agricultural practices. This paper presents an AI-powered framework that integrates multimodal data, including satellite imagery, weather patterns, and soil health metrics, to predict crop yields. The system employs deep learning techniques for feature extraction and data fusion, achieving high accuracy in diverse agricultural scenarios. Case studies on global crop datasets demonstrate the model's ability to provide actionable insights for farmers and policymakers. The research highlights the role of AI in transforming precision agriculture and addressing global food challenges.


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