{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/c57a6e6f-0271-42e5-9b8a-354f4dfa144f","identifier":"c57a6e6f-0271-42e5-9b8a-354f4dfa144f","url":"https://froggit.ai/public/capsules/c57a6e6f-0271-42e5-9b8a-354f4dfa144f","name":"Precision Agriculture Technologies Demonstrated in the Supplied Sources","text":"# Precision Agriculture Technologies Demonstrated in the Supplied Sources\n\n## Remote Sensing and Aerial Monitoring\n\n- **UV-reflectance flower detector for strawberry crop monitoring.** A remote sensing system was designed by taking inspiration from bee vision to incorporate UV-reflectance into a flower detector, with the stated aim of using technological tools in the agro-food sector to increase productivity, cut labor costs, and reduce resource use. Source: *See as a Bee: UV Sensor for Aerial Strawberry Crop Monitoring*, arXiv:2210.16923v1 — https://arxiv.org/abs/2210.16923v1\n- **Long-duration fully autonomous rotorcraft unmanned aerial systems (UAS) for remote-sensing data acquisition.** Applications of UAS to precision agriculture have shown increased ease and efficiency in data collection at precise remote locations; further enhancement of the field requires operation over long periods, such as days or weeks. Source: arXiv:1908.06381v1 — https://arxiv.org/abs/1908.06381v1\n\n## Machine Learning for Crop Disease Diagnosis\n\n- **Multimodal benchmark dataset and model for crop disease diagnosis.** Conversational generative AI has shown considerable potential for enhancing decision-making among agricultural professionals, but its exploration has predominantly been anchored in text-based interactions; the work advances multimodal conversational AI leveraging large amounts of image-text data. Source: arXiv:2503.06973v1 — https://arxiv.org/abs/2503.06973v1\n- **Data augmentation through background removal for apple leaf disease classification using the MobileNetV2 model.** Advances in computer vision made possible by deep learning are increasingly used in precision agriculture to automate detection and classification of plant diseases, whose symptoms are often seen on leaves. Source: arXiv:2412.01854v1 — https://arxiv.org/abs/2412.01854v1\n\n## Distributed Sensing and Target Localization\n\n- **Semi-parametric model for target localization in distributed systems.** Distributed","keywords":["trinity-research","sentinel_research","agriculture-food"],"about":[],"citation":["https://arxiv.org/abs/2210.16923v1","https://arxiv.org/abs/1908.06381v1","https://arxiv.org/abs/2503.06973v1","https://arxiv.org/abs/2412.01854v1","https://www.nsf.gov/science-matters/advancing-farming-cutting-edge-technologies","https://arxiv.org/abs/2012.02025v1","https://cxotoday.com/media-coverage/agritech-making-agriculture-future-ready-with-technology/","https://www.agweb.com/news/unlocking-more-less-through-precision-agriculture"],"isPartOf":{"@type":"Dataset","name":"Froggit.ai Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Froggit.ai","url":"https://froggit.ai"},"dateCreated":"2026-10-25T23:55:54.079399Z","dateModified":"2026-10-25T23:55:54.079399Z","isBasedOn":"https://arxiv.org/abs/2210.16923v1","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":100},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"institutional"},{"@type":"PropertyValue","name":"content_hash","value":"5e6ea450d0f76c94e1ff56723523b52a0e98156bd3d32779160583b54ccf5c2f"}]}