Wide use and availability of the machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new application appears also in biology and agriculture, where we could speak about the detection of infections, parasites and weeds, but also about automated monitoring and early warning systems. This is also connected with the introduction of the easily accessible hardware and development kits such as Arduino, or RaspberryPi family. In this paper, we survey 50 existing papers focusing on the methods of automated beehive monitoring methods using the computer vision techniques, particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for the monitoring of the honeybee colonies and for the inspection of their health state, which could identify potentially dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Later, we also include analysis of the research trends in this application field and we outline the possible direction of the new explorations. Our paper is aimed also at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce them to its possibilities, therefore each family of applications is opened by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring.

Bee pollen is a ball or pellet of field-gathered flower pollen packed by worker honeybees, consisting of simple sugars, protein, minerals and vitamins, fatty acids, and other components in small quantities. This is the primary food source for the hive. This article aims to use deep learning to differentiate between images of honey bees carrying pollen and those that aren't. These deep learning models can prove useful in bee farming for analysis/inference generation. This image dataset has been created from videos captured at the entrance of a bee colony in June 2017 at the Bee facility of the Gurabo Agricultural Experimental Station of the University of Puerto Rico.

In recent years extreme value mixture models have been proposed as a combination of a distribution with a "bulk part" below threshold and a generalized Pareto distribution (GPD) in the tail. Different distributions have been proposed for modelling the "bulk part" where the threshold is a parameter to be estimated. The first approach which allow us a transition between the bulk and tail parts is provided by Frigessi, Haug & Harvard (2003). Frigessi et al. (2003) uses a Weibull distribution in the bulk part, a GPD for the tail and the location-scale Cauchy cdf in the transition function and the authors use maximum likelihood estimation. However in the Frigessi et al. (2003) approach maximum likelihood estimation in the bulk part could produce multiple modes and hence some identifiability problems. Behrens, Lopez & Gammerman (2004) and Carreu & Bengio (2009) consider Gamma and Normal distributions respectively in the bulk part.