Low-Cost Electronic Tagging System for Bee Monitoring

A versatile recording device for the analysis of continuous daily external activity in colonies of highly eusocial bees

As pollinators, bees are key to maintaining the biodiversity of angiosperm plants, and for agriculture they provide a billion-dollar ecosystem service. But they also compete for resources (primarily nectar and pollen), especially the highly social bees that live in perennial colonies. So, how do they organize their daily temporal activities? Here, we present a versatile, low-cost device for the continuous, automatic recording and data analysis of the locomotor activity in the colony-entrance tube of highly eusocial bees. Consisting of an in-house built block containing an infrared detector, the passage of bees in the colony entrance tunnel is registered and automatically recorded in an Arduino environment, together with concomitant recordings of temperature and relative humidity. With a focus on the highly diverse Neotropical stingless bees (Meliponini), we obtained 10-day consecutive recordings for two colonies each of the species Melipona quadrifasciata and Frieseomelitta varia, and also for the honey bee. The Lomb-Scargle periodogram analysis identified a predominant circadian rhythmicity for all three species, but also indications of ultradian rhythms. For M. quadrifasciata, which is comparable in size to the honey bee, we found evidence for a possibly anticipatory activity already before sunrise. As all three species also presented activity at night in the colony entrance tube, this also raises questions about sleep organization in social insects. The cost and versatility of the device and the open-source options for data analysis make this an attractive system for conducting studies on circadian rhythms in social bees under natural conditions, complementing studies on flower visits by these important pollinators.

Pollinator-assisted plant phenotyping, selection, and breeding for crop resilience to abiotic stresses

Food security is threatened by climate change, with heat and drought being the main stresses affecting crop physiology and ecosystem services, such as plant-pollinator interactions. We hypothesize that tracking and ranking pollinators' preferences for flowers under environmental pressure could be used as a marker of plant quality for agricultural breeding to increase crop stress tolerance. Despite increasing relevance of flowers as the most stress sensitive organs, phenotyping platforms aim at identifying traits of resilience by assessing the plant physiological status through remote sensing-assisted vegetative indexes, but find strong bottlenecks in quantifying flower traits and in accurate genotype-to-phenotype prediction. However, as the transport of photoassimilates from leaves (sources) to flowers (sinks) is reduced in low-resilient plants, flowers are better indicators than leaves of plant well-being. Indeed, the chemical composition and amount of pollen and nectar that flowers produce, which ultimately serve as food resources for pollinators, change in response to environmental cues. Therefore, pollinators' preferences could be used as a measure of functional source-to-sink relationships for breeding decisions. To achieve this challenging goal, we propose to develop a pollinator-assisted phenotyping and selection platform for automated quantification of Genotype × Environment × Pollinator interactions through an insect geo-positioning system. Pollinator-assisted selection can be validated by metabolic, transcriptomic, and ionomic traits, and mapping of candidate genes, linking floral and leaf traits, pollinator preferences, plant resilience, and crop productivity. This radical new approach can change the current paradigm of plant phenotyping and find new paths for crop redomestication and breeding assisted by ecological decisions.

Microhabitat conditions remedy heat stress effects on insect activity

Anthropogenic global warming has major implications for mobile terrestrial insects, including long-term effects from constant warming, for example, on species distribution patterns, and short-term effects from heat extremes that induce immediate physiological responses. To cope with heat extremes, they either have to reduce their activity or move to preferable microhabitats. The availability of favorable microhabitat conditions is strongly promoted by the spatial heterogeneity of habitats, which is often reduced by anthropogenic land transformation. Thus, it is decisive to understand the combined effects of these global change drivers on insect activity. Here, we assessed the movement activity of six insect species (from three orders) in response to heat stress using a unique tracking approach via radio frequency identification. We tracked 465 individuals at the iDiv Ecotron across a temperature gradient up to 38.7°C. In addition, we varied microhabitat conditions by adding leaf litter from four different tree species to the experimental units, either spatially separated or well mixed. Our results show opposing effects of heat extremes on insect activity depending on the microhabitat conditions. The insect community significantly decreased its activity in the mixed litter scenario, while we found a strong positive effect on activity in the separated litter scenario. We hypothesize that the simultaneous availability of thermal refugia as well as resources provided by the mixed litter scenario allows animals to reduce their activity and save energy in response to heat stress. Contrary, the spatial separation of beneficial microclimatic conditions and resources forces animals to increase their activity to fulfill their energetic needs. Thus, our study highlights the importance of habitat heterogeneity on smaller scales, because it may buffer the consequences of extreme temperatures of insect performance and survival under global change.

Automated Video Monitoring of Unmarked and Marked Honey Bees at the Hive Entrance

We present a novel system for the automatic video monitoring of honey bee foraging activity at the hive entrance. This monitoring system is built upon convolutional neural networks that perform multiple animal pose estimation without the need for marking. This precise detection of honey bee body parts is a key element of the system to provide detection of entrance and exit events at the entrance of the hive including accurate pollen detection. A detailed evaluation of the quality of the detection and a study of the effect of the parameters are presented. The complete system also integrates identification of barcode marked bees, which enables the monitoring at both aggregate and individual levels. The results obtained on multiple days of video recordings show the applicability of the approach for large-scale deployment. This is an important step forward for the understanding of complex behaviors exhibited by honey bees and the automatic assessment of colony health.

Correlation Between Increased Homing Flight Duration and Altered Gene Expression in the Brain of Honey Bee Foragers After Acute Oral Exposure to Thiacloprid and Thiamethoxam

Neonicotinoids as thiamethoxam and thiacloprid are suspected to be implicated in the decline of honey bee populations. As nicotinic acetylcholine receptor agonists, they disturb acetylcholine receptor signaling in insects, leading to neurotoxicity and are therefore globally used as insecticides. Several behavioral studies have shown links between neonicotinoid exposure of bees and adverse effects on foraging activity, homing flight performance and reproduction, but the molecular aspects underlying these effects are not well-understood. In the last years, several studies through us and others showed the effects of exposure to neonicotinoids on gene expression in the brain of honey bees. Transcripts of acetylcholine receptors, hormonal regulation, stress markers, detoxification enzymes, immune system related genes and transcripts of the energy metabolism were altered after neonicotinoid exposure. To elucidate the link between homing flight performance and shifts in gene expression in the brain of honey bees after neonicotinoid exposure, we combined homing flight activity experiments applying RFID technology and gene expression analysis. We analyzed the expression of endocrine factors, stress genes, detoxification enzymes and genes linked to energy metabolism in forager bees after homing flight experiments. Three different experiments (experiment I: pilot study; experiment II: "worst-case" study and experiment III: laboratory study) were performed. In a pilot study, we wanted to investigate if we could see differences in gene expression between controls and exposed bees (experiment I). This first study was followed by a so-called "worst-case" study (experiment II), where we investigated mainly differences in the expression of transcripts linked to energy metabolism between fast and slow returning foragers. We found a correlation between homing flight duration and the expression of cytochrome c oxidase subunit 5A, one transcript linked to oxidative phosphorylation. In the third experiment (experiment III), foragers were exposed in the laboratory to 1 ng/bee thiamethoxam and 8 ng/bee thiacloprid followed by gene expression analysis without a subsequent flight experiment. We could partially confirm the induction of cytochrome c oxidase subunit 5A, which we detected in experiment II. In addition, we analyzed the effect of the feeding mode (group feeding vs. single bee feeding) on data scattering and demonstrated that single bee feeding is superior to group feeding as it significantly reduces variability in gene expression. Based on the data, we thus hypothesize that the disruption of energy metabolism may be one reason for a prolongation of homing flight duration in neonicotinoid treated bees.

RFID Technology Serving Honey Bee Research: A Comprehensive Description of a 32-Antenna System to Study Honey Bee and Queen Behavior

The fields of electronics and information technology have witnessed rapid development during the last decades, providing significant technical support to the field of biological sciences. Radio-Frequency Identification (RFID) technology has been used to automate the monitoring of animal location and behaviors in a wide range of vertebrate and invertebrate species, including social insects such as ants and honey bees (Apis mellifera L.). This technology relies on electromagnetic fields to identify and track transponders attached to objects automatically. Implementing new technologies to serve research purposes could be time consuming and require technical expertise from entomologists and researchers. Herein, we present a detailed description on how to harness RFID technology to serve honey bee research effectively. We describe how to build and operate a 32-antenna RFID system used to monitor various honey bee behaviors such as foraging, robbing, and queen and drone mating, which can be used in other social insects as well. Preliminary data related to queen nuptial flights were obtained using this unit and presented in this study. Virgin queens labeled with ≈5 mg transponders performed multiple (one to four) nuptial/orientation flights a day (9 a.m. to 5 p.m.) ranging from 8 to 145 s each. Contrary to virgin queens, no hive exit was recorded for mated queens. At full capacity, this unit can monitor up to 32 honey bee colonies concurrently and is self-sustained by a solar panel to work in remote areas. All materials, hardware, and software needed to build and operate this unit are detailed in this study, offering researchers and beekeepers a practical solution and a comprehensive source of information enabling the implementation of RFID technology in their research perspective.

Estimating the effect of tracking tag weight on insect movement using video analysis: A case study with a flightless orthopteran

In this study, we describe an inexpensive and rapid method of using video analysis and identity tracking to measure the effects of tag weight on insect movement. In a laboratory experiment, we assessed the tag weight and associated context-dependent effects on movement, choosing temperature as a factor known to affect insect movement and behavior. We recorded the movements of groups of flightless adult crickets Gryllus locorojo (Orthoptera:Gryllidae) as affected by no tag (control); by light, medium, or heavy tags (198.7, 549.2, and 758.6 mg, respectively); and by low, intermediate, or high temperatures (19.5, 24.0, and 28.3°C, respectively). Each individual in each group was weighed before recording and was recorded for 3 consecutive days. The mean (± SD) tag mass expressed as a percentage of body mass before the first recording was 26.8 ± 3.7% with light tags, 72 ± 11.2% with medium tags, and 101.9 ± 13.5% with heavy tags. We found that the influence of tag weight strongly depended on temperature, and that the negative effects on movement generally increased with tag weight. At the low temperature, nearly all movement properties were negatively influenced. At the intermediate and high temperatures, the light and medium tags did not affect any of the movement properties. The continuous 3-day tag load reduced the average movement speed only for crickets with heavy tags. Based on our results, we recommend that researchers consider or investigate the possible effects of tags before conducting any experiment with tags in order to avoid obtaining biased results.

Analysis of Honeybee Drone Activity during the Mating Season in Northwestern Argentina

Males in Hymenopteran societies are understudied in many aspects and it is assumed that they only have a reproductive function. We studied the time budget of male honey bees, drones, using multiple methods. Changes in the activities of animals provide important information on biological clocks and their health. Yet, in nature, these changes are subtle and often unobservable without the development and use of modern technology. During the spring and summer mating season, drones emerge from the hive, perform orientation flights, and search for drone congregation areas for mating. This search may lead drones to return to their colony, drift to other colonies (vectoring diseases and parasites), or simply get lost to predation. In a low percentage of cases, the search is successful, and drones mate and die. Our objective was to describe the activity of Apis mellifera drones during the mating season in Northwestern Argentina using three methods: direct observation, video recording, and radio frequency identification (RFID). The use of RFID tagging allows the tracking of a bee for 24 h but does not reveal the detailed activity of drones. We quantified the average number of drones' departure and arrival flights and the time outside the hive. All three methods confirmed that drones were mostly active in the afternoon. We found no differences in results between those obtained by direct observation and by video recording. RFID technology enabled us to discover previously unknown drone behavior such as activity at dawn and during the morning. We also discovered that drones may stay inside the hive for many days, even after initiation of search flights (up to four days). Likewise, we observed drones to leave the hive for several days to return later (up to three days). The three methods were complementary and should be considered for the study of bee drone activity, which may be associated with the diverse factors influencing hive health.

The Lifelog Monitoring System for Honeybees: RFID and Camera Recordings in an Observation Hive

A typical honeybee colony contains more than 15,000 individuals, each with its own task related to supporting the hive and maintaining the colony. In previous studies on honeybees, observing individual animals’ behaviors has been a difficult and time-consuming task to understand the relationship between in-hive communication and environmental changes outside the hive, therefore it is necessary in any attempt to develop applying a remote sensing technology. To allow researchers to pass much of this tracking work on to computers, we have developed the lifelog monitoring system for honeybees, which uses RFID and Raspberry Pi camera recordings. Our preliminary experiments consisted of several tests aimed at identifying the optimal conditions for this system. First, two commercial RFID readers with antennas were compared in terms of their sensitivity to signals from RFID tags placed at various distances. We found that the UP16-1000-J2 reader was much more sensitive and had a longer effective range compared to the UP4-200-J2. The most sensitive region in the RFID antenna on the UP16-1000-J2 reader was 30 mm long and 5 mm wide at its center. Based on this preliminary information, we designed and built a passage from the interior of the observation hive to the outside so that all RFID-tagged bees could be detected individually by the RFID reader as they walked through the passage. Moreover, to detect the direction of either departure or arrival of each bee, we placed two RFID antennas under the passage between the observation hive and the outside, one near each end of the passage. All departure and arrival times of RFID-tagged bees were detected with their ID numbers. Using recorded data from these two RFID readers, we could measure how much time each tagged bee spent outside the hive. In addition to RFID recording on the passage, we also tracked all in-hive movements of numbered RFID-tagged honeybees. In-hive movements were simultaneously, comprehensively and automatically recorded via six Raspberry Pi camera modules arranged on the two sides of the observation hive. The cameras were set to record from 6:30 to 19:30 every day for one month, once or twice each year from 2015 to 2018. The in-hive behaviors of these bees were analyzed according to a simultaneous tracking algorithm that we developed for this purpose. Data from the monitoring system revealed that time spent outside the hive increased markedly after following the waggle dance. In addition to its findings on bee behavior, this study also confirms the effectiveness of our recording system combining RFID and Raspberry Pi cameras for honeybee lifelog monitoring.

Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques

The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and the bee colonies have to be regularly monitored in order to control its spread. In this paper we present an object detector based method for health state monitoring of bee colonies. This method has the potential for online measurement and processing. In our experiment, we compare the YOLO and SSD object detectors along with the Deep SVDD anomaly detector. Based on the custom dataset with 600 ground-truth images of healthy and infected bees in various scenes, the detectors reached the highest F1 score up to 0.874 in the infected bee detection and up to 0.714 in the detection of the Varroa destructor mite itself. The results demonstrate the potential of this approach, which will be later used in the real-time computer vision based honey bee inspection system. To the best of our knowledge, this study is the first one using object detectors for the Varroa destructor mite detection on a honey bee. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies in real time.

System design for inferring colony-level pollination activity through miniature bee-mounted sensors

In digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems.

A Novel Charging Method for Underwater Batteryless Sensor Node Networks

In this paper, we present a novel charging method for underwater batteryless sensor node networks. The target application is a practical underwater sensor network for oceanic fish farms. The underwater sections of the network use a wireless power transfer system based on the ISO 11784/11785 HDX standard for supplying energy to the batteryless sensor nodes. Each sensor has an accumulator capacitor, which is charged for voltage supplying to the sensor node. A new distributed charging scheme is proposed and discussed in detail to reduce the required time to charge all sensor nodes of the underwater sections. One important key is its decentralized control of the charging process. The proposal is based on the self disconnection ability of each sensor node from the charging network. The second important key is that the hardware implementation of this new feature is quite simple and only requires to include a minimal circuitry in parallel to the current sensor node antenna while the rest of the sensor network remains unaltered. The proposed charging scheme is evaluated using real corner cases from practical oceanic fish farms sensor networks. The results from experiments demonstrate that it is possible to charge up to 10 sensor nodes which is the double charging capability than previous research presented. In the same conditions as the approach found in the literature, it represents reaching an ocean depth of 60 m. In terms of energy, in case of an underwater network with 5 sensors to reach 30 m deep, the proposed charging scheme requires only a 25% of the power required using the traditional approach.

Technological advances in field studies of pollinator ecology and the future of e-ecology

Our review looks at recent advances in technologies applied to studying pollinators in the field. These include RFID, radar and lidar for detecting and tracking pollinators; wireless sensor networks (e.g. 'smart' hives); automated visual and audio monitoring systems including vision motion software for monitoring fine-scale pollinator behaviours over extended periods; and automated species identification systems based on machine learning that can vastly reduce the bottleneck in (big) data analysis. An improved e-ecology platform that leverages these tools is needed for ecologists to acquire and understand large spatiotemporal datasets, and thus inform knowledge gaps in environmental policy-making. Developing the next generation of e-ecology tools will require synergistic partnerships between academia and industry and significant investment in a cross-disciplinary scientific consortia.

Cost-Effective Implementation of a Temperature Traceability System Based on Smart RFID Tags and IoT Services

This paper presents the design and validation of a traceability system, based on radio frequency identification (RFID) technology and Internet of Things (IoT) services, intended to address the interconnection and cost-implementation problems typical in traceability systems. The RFID layer integrates temperature sensors into RFID tags, to track and trace food conditions during transportation. The IoT paradigm makes it possible to connect multiple systems to the same platform, addressing interconnection problems between different technology providers. The cost-implementation issues are addressed following the Data as a Service (DaaS) billing scheme, where users pay for the data they consume and not the installed equipment, avoiding the big initial investment that these high-tech solutions commonly require. The developed system is validated in two case scenarios, one carried out in controlled laboratory conditions, monitoring chopped pumpkin. Another case, carried out in a real scenario, monitors oranges sent from Valencia, Spain to Cork, Ireland.

An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection

Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of the regular behavior and the adverse situations that may occur with the bees. It also may lead to better management and utilization of bees as pollinators. We address an investigation with Recurrent Neural Networks in the task of forecasting bees’ level of activity taking into account previous values of level of activity and environmental data such as temperature, solar irradiance and barometric pressure. We also show how different input time windows, algorithms of attribute selection and correlation analysis can help improve the accuracy of our model.

Weighted Clustering for Bees Detection on Video Images

This work describes a bee detection system to monitor bee colony conditions. The detection process on video images has been divided into 3 stages: determining the regions of interest (ROI) for a given frame, scanning the frame in ROI areas using the DNN-CNN classifier, in order to obtain a confidence of bee occurrence in each window in any position and any scale, and form one detection window from a cloud of windows provided by a positive classification. The process has been performed by a method of weighted cluster analysis, which is the main contribution of this work. The paper also describes a process of building the detector, during which the main challenge was the selection of clustering parameters that gives the smallest generalization error.

The results of the experiments show the advantage of the cluster analysis method over the greedy method and the advantage of the optimization of cluster analysis parameters over standard-heuristic parameter values, provided that a sufficiently long learning fragment of the movie is used to optimize the parameters.

False Positive RFID Detection Using Classification Models

Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.