Global Biotic Interactions provides access to existing species interaction datasets
Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring
Melittoflora is a data exploration, visualization, and analytical interface that empowers users to explore species occurrence records of bees interacting with flowering plants, visualize their complex networks, and output a variety of informative products.
This repository contains the scripts used to clean and process the spatial data of wild bees and hoverflies from the SAFEGUARD project. For more information, please see the data paper. The code is intended for reuse and modification under the MIT license, provided proper attribution is given.
Apps to inform people about wild pollinators importance in Europe. The apps are prototypes based on dummy data and developed in R with Shiny package. Funded by the EU Horizon project "Butterfly".
Data visualization app for WiBee pollinator surveys.
Guide for vegetable farmers to integrate pollinator management through beekeeping, habitat creation & integrated crop pollination. Includes economic models, implementation strategies, training resources & case studies showing 25-30% yield increases plus additional income from honey production and pollination services.
Hive management, varroa mite monitoring, swarm prevention, honey yield tracking, pollinator integration with crops. Bridges animals + spatial + (optionally)commerce
App suggesting a plant list for each EU NUT region to support local pollinator species. Prototype in R using Shiny package. Based on occurence and Plant-Pollinator interaction data published by Euroappa (https://github.com/euroappa/euroappa.github.io). Plant traits based on dummy data. Funded by EU Horizon Project "Butterfly".
Updated workflow to publish pollinator data to GBIF
Computer-vision pipeline to detect, track, count and classify insects (bee focus) into pollinators vs non-pollinators, linking activity to pollination rate.
Plataforma modular d'apicultura de precisió per al monitoratge, l'anàlisi i la predicció de l'estat dels ruscs mitjançant IoT, processament acústic i aprenentatge automàtic.
This page was created as a capstone project for the class "Web Development for Everybody" (University of Michigan via Coursera)
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