<ul data-eligibleForWebStory="true"><li>Agriculture, rich in biodiversity, often lacks representation in typical AI training data, posing challenges for models like CLIP.</li><li>Experiment using BioTrove dataset showcases the potential of AI in agriculture and biodiversity.</li><li>BioTrove includes 161 million labeled images, supporting AI in agriculture, biodiversity, and conservation.</li><li>CLIP models in BioTrove excel in underrepresented categories like insects, birds, fungi, and native plants.</li><li>BioTrove is a resource for AI tools supporting crop health, pest monitoring, and environmental research.</li><li>Data-centric approach using foundation models like CLIP helps identify patterns and blind spots in datasets.</li><li>Models combined with human expertise aid in creating fairer datasets reflecting biodiversity for better outcomes.</li><li>Importance of prioritizing underrepresented species and quality data in AI for agriculture and conservation.</li><li>Data-centric AI emphasizes curating right data for underrepresented regions, species, and scenarios.</li><li>Evaluation of models like CLIP with rich metadata and filters assists in improving data quality interactively.</li></ul>

Biodiversity Meets Foundation Models

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