There are 1 repository under beehive topic.
Python scripts that are executed on the hive scale for data acquisition in measurement mode.
Ansible role to install beehive https://github.com/muesli/beehive
ESP8266 based bee hive scale with MQTT transmission based on homie-esp8266
An Hive Montor based on Homie for an Arduino devices or a similar one
A sample repo demonstrates how to build a modular iOS project using BeeHive and MGJRouter
Version 1 of a BeeHive Scale with GSM trasmitter based on Arduino. Code and report(in Greek)
Včelárska váha - Beehive weight - Bienenzuchtwaage - Arduino / ESP8266 / ESP32 - Ethernet, WiFi.
Bee hive weight measuring equipment with information transfer via Internet of Things network Sigfox
The website for Scanning Bee.
Projets du Lycée des Andaines de la Ferté Massey.
[CONTRIB FORK] Admin interface for Beehive
Beehive online monitoring based on the ESP32, SIM800L and others sensors.
A beehive monitoring system using high quality camera and some sensors to detect bee absconding.
A sustainable and ecologically conscious bee husbandry system designed for rational bee keeping.
This app is a group project where a user can log in and create a project for others to view and connect for volunteer opportunities
Application web dédié aux apiculteurs. Projet CodeIgniter
In this learning project, l have explored a dataset with annotated images of bees from various locations of US, captured over several months during 2018, at different hours, from various bees subspecies, and with different health problems. The objective is to do Exploratory Data Analysis, features engineering and develop a CNN model to classify the bees subspecies.
GreenBee est un projet innovant visant à révolutionner l'apiculture en utilisant des technologies avancées pour améliorer la production de miel, surveiller la santé des abeilles, et favoriser la durabilité environnementale. Toute en intégrant des capteurs, l'intelligence artificielle (IA), l'imagerie satellite, et une application web/mobile.
Este repositorio contiene el código, datos y recursos utilizados en el estudio "Machine Learning en la Apicultura: Clasificación Avanzada del Estado de Colmenas con Pipeline Optimizado y Etiquetado Preciso" por Axel A. Skrauba, Hector De Sosa, y Sandro D. Zakowicz.
Predict bee phenotype, beehive health as well as live time bee and beehive Monitoring