The rapid development of machine learning and deep learning methodologies in agriculture has led to significant advancements in smart crop management, plant breeding, livestock farming, aquaculture, and agricultural robotics. Traditional ML/DL models face limitations such as reliance on labeled datasets, specialized expertise, and lack of generalizability. Large pre-trained models or foundation models (FMs) have shown success across various domains by training on vast data from multiple sources. These FMs can perform diverse tasks with minor fine-tuning and minimal labeled data. Despite their effectiveness, there is limited exploration of applying FMs in agriculture AI. This study aims to explore the potential of FMs in smart agriculture by categorizing them into language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs.
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by Jiajia Li,Mi... às arxiv.org 03-19-2024
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