Multimodal Framework for Agriculture Analysis using AI

Agriculture plays a vital role in ensuring food security and economic development, but traditional farming practices often face challenges such as crop diseases, unpredictable weather conditions, soil degradation, and inefficient resource utilization. Recent advancements in Artificial Intelligence (AI) have enabled the development of intelligent systems that assist farmers in making timely and data-driven decisions. This research proposes a Multimodal AI Framework for Agriculture Analysis that integrates multiple data sources, including crop images, soil parameters, weather information, and sensor data, to provide comprehensive agricultural insights. The framework employs deep learning models for crop disease detection, machine learning algorithms for yield prediction, and data fusion techniques to combine information from different modalities for accurate decision-making. The proposed system aims to recommend suitable fertilizers, irrigation schedules, and disease management strategies while improving crop productivity and resource efficiency. Performance evaluation is carried out using metrics such as accuracy, precision, recall, F1-score, and processing time. The experimental analysis demonstrates that the multimodal approach provides higher prediction accuracy and better decision support than single-data-source models.