Honeybees are one of the most important pollinating species in agriculture. Every three out of four crops have honeybee as their sole pollinator. Since 2006 there has been a drastic decrease in the bee population which is attributed to Colony Collapse Disorder(CCD). The bee colonies fail/ die without giving any traditional health symptoms which otherwise could help in alerting the Beekeepers in advance about their situation. Electronic Beehive Monitoring System has various sensors embedded in it to extract video, audio and temperature data that could provide critical information on colony behavior and health without invasive beehive inspections. Previously, significant patterns and information have been extracted by processing the video/image data, but no work has been done using audio data. This research inaugurates and takes the first step towards the use of audio data in the Electronic Beehive Monitoring System (BeePi) by enabling a path towards the automatic classification of audio samples in different classes and categories within it. The experimental results give an initial support to the claim that monitoring of bee buzzing signals from the hive is feasible, it can be a good indicator to estimate hive health and can help to differentiate normal behavior against any deviation for honeybees.

Thesis (M.A.)--Brigham Young University Graduate Dept. of Art. / Electronic thesis. Also available in print ed.

No / The papers collected in this volume are a selection from the proceedings of the Cultura Popular conference held at Manchester Metropolitan University in September 1999. The essays deal with aspects of contemporary Spanish or Latin American popular culture, and with the problematics of applying theories of Cultural Studies to these contexts. A diverse range of popular cultural forms is covered by contributors including mural art, artesanía, horror film, advertising, music, telenovela, television, literature and tourism, and case studies are drawn from Spain, Argentina, Peru and Mexico.

This thesis describes the power analysis of the electronic beehive monitoring system. The electronic beehive monitoring system was made to work either with a UB12120 12V 12Ah standard lead-acid battery or an Anker (TM) Astro E7 5V lithium-ion battery to analyze the power requirements. These batteries are recharged by Renogy 50Watt 12 Volt Monocrystalline Solar Panel. Power analysis is performed using both batteries to calculate system’s efficiency. The performed power analysis indicates that the Anker (TM) Astro E7 26800mAh 5V lithium-ion battery runs approximately 6 hours more than the lead acid battery. Moreover, the lithium-ion battery is compact, has a lighter weight, is more efficient, and has a longer cycle life. Using lithium-ion batteries will likely result in fewer hardware components and a smaller environmental footprint.

Thesis (M.A.)-- Brigham Young University Graduate Dept. of Art. / Includes bibliographical references (leaves 128-131).

This article consists on the study of how beekeeping can contribute to the economic development of small agricultural communities. The case study is the Retirense Association of Beekeepers (ARAPI) established by inhabitants of the «Retiro São Bento» community, located in the «Pantanal» in the city Barão de Melgaço in Mato Grosso, Brazil. The objective is to study the structure of the productive net of this organization as well as its importance for the development of the region where it is inserted. The research has been based on observation in loco and in information in documents yielded by Association and Ecological Ranch «SESC Pantanal». This is the entity that promoted the first actions for the insertion of the beekeeping in the «Retiro São Bento» community. As it has been already pointed in some studies, the Pantanal is an ecosystem of beehive, and then, the ARAPI has gotten success in its production and consequently the community could increase the familiar income, and promote actions for the development of the beekeeping as well as to guarantee a longer time of the biological wealth of this environmental «pantaneiro» to the next generations. / Este artigo constitui-se no estudo de como a apicultura pode contribuir para o desenvolvimento econômico de pequenas comunidades rurais, em especial no caso da Associação Retirense de Apicultores (ARAPI) fundada por moradores da comunidade Retiro São Bento localizada no Pantanal do município de Barão de Melgaço em Mato Grosso. Tendo como objetivo estudar a estrutura da rede produtiva desta organização bem como sua importância para o desenvolvimento da região em que está inserida, os meios de investigação se basearam em observação in loco e coleta de informações em documentos cedidos pela Associação e pela estância Ecológica SESC Pantanal, entidade que promoveu as primeiras ações para a inserção da apicultura na comunidade Retiro São Bento. Como já apontado em alguns estudos, o Pantanal é um ecossistema de grande potencial apícola, desta forma, a ARAPI tem obtido êxito em suas produções e, consequentemente a comunidade pôde incrementar a renda familiar e promover ações conservacionistas em benefício tanto para o desenvolvimento da apicultura como também para garantir por maior tempo a riqueza biológica deste ambiente pantaneiro às próximas gerações.

The diploma thesis is about designing device which monitors temperature and humidity. In the first part of thesis are explained basic terms, importance of key variables and on market offered products are described. After that the thesis describes device concept design with schematic designs. From designed schematics are described printed circuit board designs. Next part is about device commissioning and correction of errors caused by incorrect design. Last part is about program designing and device testing.

An algorithm is presented to count bee numbers in images of Langsthroth hive entrances. The algorithm computes approximate bee counts by adjusting the brightness of the image, cropping a white or green area in the image, removing the background and noise from the cropped area, finding the total number of bee pixels, and dividing that number by the average number of pixels in a single bee. On 1005 images with green landing pads, the algorithm achieved an accuracy of 80 percent when compared to the human bee counting. On 776 images with white landing pads, the algorithm achieved an accuracy of 85% compared to the human bee counting.

This thesis deals with creating hardware and software equipment of intelligent beehive for data collection purpose. Measurement characteristics are temperature, humidity, weight of beehive and audio record of bees. They are measured by available IoT technology. They are simple single-chip component with a clear focus. This thesis also include creation of software for measurements this characteristics and communications with server. The software featured of the remote server are also documented. This software analyze incoming measurement and also provides their displays. Software in this thesis is developed in language Python.

Ce manuscrit propose une approche méthodologique pour la constitution d’une chaîne complète de vidéosurveillance pour des environnements naturellement encombrés. Nous identifions et levons un certain nombre de verrous méthodologiques et technologiques inhérents : 1) à l’acquisition de séquences vidéo en milieu naturel, 2) au traitement d’images, 3) au suivi multi-cibles, 4) à la découverte et la modélisation de motifs comportementaux récurrents, et 5) à la fusion de données. Le contexte applicatif de nos travaux est la surveillance apicole, et en particulier, l’étude des trajectoires des abeilles en vol devant la ruche. De ce fait, cette thèse se présente également comme une étude de faisabilité et de prototypage dans le cadre des deux projets interdisciplinaires EPERAS et RISQAPI (projets menées en collaboration avec l’INRA Magneraud et le Muséum National d’Histoire Naturelle). Il s’agit pour nous informaticiens et pour les biologistes qui nous ont accompagnés, d’un domaine d’investigation totalement nouveau, pour lequel les connaissances métiers, généralement essentielles à ce genre d’applications, restent encore à définir. Contrairement aux approches existantes de suivi d’insectes, nous proposons de nous attaquer au problème dans l’espace à trois dimensions grâce à l’utilisation d’une caméra stéréovision haute fréquence. Dans ce contexte, nous détaillons notre nouvelle méthode de détection de cibles appelée segmentation HIDS. Concernant le calcul des trajectoires, nous explorons plusieurs approches de suivi de cibles, s’appuyant sur plus ou moins d’a priori, susceptibles de supporter les conditions extrêmes de l’application (e.g. cibles nombreuses, de petite taille, présentant un mouvement chaotique). Une fois les trajectoires collectées, nous les organisons selon une structure de données hiérarchique et mettons en œuvre une approche Bayésienne non-paramétrique pour la découverte de comportements émergents au sein de la colonie d’insectes. L’analyse exploratoire des trajectoires issues de la scène encombrée s’effectue par classification non supervisée, simultanément sur des niveaux sémantiques différents, et où le nombre de clusters pour chaque niveau n’est pas défini a priori mais est estimé à partir des données. Cette approche est dans un premier temps validée à l’aide d’une pseudo-vérité terrain générée par un Système Multi-Agents, puis dans un deuxième temps appliquée sur des données réelles. / This manuscript provides the basis for a complete chain of videosurveillence for naturally cluttered environments. In the latter, we identify and solve the wide spectrum of methodological and technological barriers inherent to : 1) the acquisition of video sequences in natural conditions, 2) the image processing problems, 3) the multi-target tracking ambiguities, 4) the discovery and the modeling of recurring behavioral patterns, and 5) the data fusion. The application context of our work is the monitoring of honeybees, and in particular the study of the trajectories bees in flight in front of their hive. In fact, this thesis is part a feasibility and prototyping study carried by the two interdisciplinary projects EPERAS and RISQAPI (projects undertaken in collaboration with INRA institute and the French National Museum of Natural History). It is for us, computer scientists, and for biologists who accompanied us, a completely new area of investigation for which the scientific knowledge, usually essential for such applications, are still in their infancy. Unlike existing approaches for monitoring insects, we propose to tackle the problem in the three-dimensional space through the use of a high frequency stereo camera. In this context, we detail our new target detection method which we called HIDS segmentation. Concerning the computation of trajectories, we explored several tracking approaches, relying on more or less a priori, which are able to deal with the extreme conditions of the application (e.g. many targets, small in size, following chaotic movements). Once the trajectories are collected, we organize them according to a given hierarchical data structure and apply a Bayesian nonparametric approach for discovering emergent behaviors within the colony of insects. The exploratory analysis of the trajectories generated by the crowded scene is performed following an unsupervised classification method simultaneously over different levels of semantic, and where the number of clusters for each level is not defined a priori, but rather estimated from the data only. This approach is has been validated thanks to a ground truth generated by a Multi-Agent System. Then we tested it in the context of real data.