The Effectiveness of Varroa destructor Infestation Classification Using an E-Nose Depending on the Time of Day

Automated, non-invasive Varroa mite detection by vibrational measurements of gait combined with machine learning

Little is known about mite gait, but it has been suggested that there could be greater variation in locomotory styles for arachnids than insects. The Varroa destructor mite is a devastating ectoparasite of the honeybee. We aim to automatically detect Varroa-specific signals in long-term vibrational recordings of honeybee hives and additionally provide the first quantification and characterisation of Varroa gait through the analysis of its unique vibrational trace. These vibrations are used as part of a novel approach to achieve remote, non-invasive Varroa monitoring in honeybee colonies, requiring discrimination between mite and honeybee signals. We measure the vibrations occurring in samples of freshly collected capped brood-comb, and through combined critical listening and video recordings we build a training database for discrimination and classification purposes. In searching for a suitable vibrational feature, we demonstrate the outstanding value of two-dimensional-Fourier-transforms in invertebrate vibration analysis. Discrimination was less reliable when testing datasets comprising of Varroa within capped brood-cells, where Varroa induced signals are weaker than those produced on the cell surface. We here advance knowledge of Varroa vibration and locomotion, whilst expanding upon the remote detection strategies available for its control.

Volatile biomarkers for non-invasive detection of American foulbrood, a threat to honey bee pollination services

Honey bees provide essential environmental services, pollinating both agricultural and natural ecosystems that are crucial for human health. However, these pollination services are under threat by outbreaks of the bacterial honey bee disease American foulbrood (AFB). Caused by the bacterium, Paenibacillus larvae, AFB kills honey bee larvae, converting the biomass to a foul smelling, spore-laden mass. Due to the bacterium's tough endospores, which are easily spread and extremely persistent, AFB management requires the destruction of infected colonies in many countries. AFB detection remains a significant problem for beekeepers: diagnosis is often slow, relying on beekeepers visually identifying symptoms in the colony and molecular confirmation. Delayed detection can result in large outbreaks during high-density beekeeping pollination events, jeopardising livelihoods and food security. In an effort to improve diagnostics, we investigated volatile compounds associated with AFB-diseased brood in vitro and in beehive air. Using Solid Phase Microextraction and Gas Chromatography Mass-Spectrometry, we identified 40 compounds as volatile biomarkers for AFB infections, including 16 compounds previously unreported in honey bee studies. In the field, we detected half of the biomarkers in situ (in beehive air) and demonstrated their sensitivity and accuracy for diagnosing AFB. The most sensitive volatile biomarker, 2,5-dimethylpyrazine, was exclusively detected in AFB-disease larvae and hives, and was detectable in beehives with <10 AFB-symptomatic larvae. These, to our knowledge, previously undescribed biomarkers are prime candidates to be targeted by a portable sensor device for rapid and non-invasive diagnosis of AFB in beehives.

Deep Learning Beehive Monitoring System for Early Detection of the Varroa Mite

One of the most critical causes of colony collapse disorder in beekeeping is caused by the Varroa mite. This paper presents an embedded camera module supported by a deep learning algorithm for the process of early detecting of Varroa infestations. This is achieved using a deep learning algorithm that tries to identify bees inside the brood frames carrying the mite in real-time. The end-node device camera module is placed inside the brood box. It is equipped with offline detection in remote areas of limited network coverage or online imagery data transmission and mite detection over the cloud. The proposed deep learning algorithm uses a deep learning network for bee object detection and an image processing step to identify the mite on the previously detected objects. Finally, the authors present their proof of concept experimentation of their approach that can offer a total bee and varroa detection accuracy of close to 70%. The authors present in detail and discuss their experimental results.

Varroa destructor from the Laboratory to the Field: Control, Biocontrol and IPM Perspectives-A Review

Varroa destructor is a real challenger for beekeepers and scientists: fragile out of the hive, tenacious inside a bee colony. From all the research done on the topic, we have learned that a better understanding of this organism in its relationship with the bee but also for itself is necessary. Its biology relies mostly on semiochemicals for reproduction, nutrition, or orientation. Many treatments have been developed over the years based on hard or soft acaricides or even on biocontrol techniques. To date, no real sustainable solution exists to reduce the pressure of the mite without creating resistances or harming honeybees. Consequently, the development of alternative disruptive tools against the parasitic life cycle remains open. It requires the combination of both laboratory and field results through a holistic approach based on health biomarkers. Here, we advocate for a more integrative vision of V. destructor research, where in vitro and field studies are more systematically compared and compiled. Therefore, after a brief state-of-the-art about the mite's life cycle, we discuss what has been done and what can be done from the laboratory to the field against V. destructor through an integrative approach.

Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It to Fusarium oxysporum and Rhizoctonia solani

Electronic noses can be applied as a rapid, cost-effective option for several applications. This paper presents the results of measurements of samples of two pathogenic fungi, Fusarium oxysporum and Rhizoctonia solani, performed using two constructions of a low-cost electronic nose. The first electronic nose used six non-specific Figaro Inc. metal oxide gas sensors. The second one used ten sensors from only two models (TGS 2602 and TGS 2603) operating at different heater voltages. Sets of features describing the shapes of the measurement curves of the sensors' responses when exposed to the odours were extracted. Machine learning classification models using the logistic regression method were created. We demonstrated the possibility of applying the low-cost electronic nose data to differentiate between the two studied species of fungi with acceptable accuracy. Improved classification performance could be obtained, mainly for measurements using TGS 2603 sensors operating at different voltage conditions.

Beehive Air Sampling and Sensing Device Operation in Apicultural Applications-Methodological and Technical Aspects

The basis of effective beekeeping is the information about the state of the bee colony. A rich source of respective information is beehive air. This source may be explored by applying gas sensing. It allows for classifying bee colony states based on beehive air measurements. In this work, we discussed the essential aspects of beehive air sampling and sensing device operation in apicultural applications. They are the sampling method (diffusive vs. dynamic, temporal aspects), sampling system (sample probe, sampling point selection, sample conditioning unit and sample delivery system) and device operation mode ('exposure-cleaning' operation). It was demonstrated how factors associated with the beehive, bee colony and ambient environment define prerequisites for these elements of the measuring instrument. These requirements have to be respected in order to assure high accuracy of measurement and high-quality information. The presented results are primarily based on the field measurement study performed in summer 2020, in three apiaries, in various meteorological conditions. Two exemplars of a prototype gas sensing device were used. These sensor devices were constructed according to our original concept.

Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

Compared with traditional gas chromatography-mass spectrometry techniques, electronic noses are non-invasive and can be a rapid, cost-effective option for several applications. This paper presents comparative studies of differentiation between odors emitted by two forest pathogens: Pythium and Phytophthora, measured by a low-cost electronic nose. The electronic nose applies six non-specific Figaro Inc. metal oxide sensors. Various features describing shapes of the measurement curves of sensors' response to the odors' exposure were extracted and used for building the classification models. As a machine learning algorithm for classification, we use the Support Vector Machine (SVM) method and various measures to assess classification models' performance. Differentiation between Phytophthora and Pythium species has an important practical aspect allowing forest practitioners to take appropriate plant protection. We demonstrate the possibility to recognize and differentiate between the two mentioned species with acceptable accuracy by our low-cost electronic nose.