Approaches, challenges and recent advances in automated bee counting devices: A review

Odemer, Richard

doi:10.1111/aab.12727

Cited by 36 publications

(40 citation statements)

References 69 publications

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“…Confirming the accuracy of filtering remains one of the limitations of RFID and QR code systems. Visually validating that a bee performed a full trip is difficult in large bee colonies (Odemer, 2022) and so there is no perfect measure to compare systems. However, with high tag detection accuracy and high bee “funneling” success, there remains little room for incorrect filtering.…”

Section: Discussionmentioning

confidence: 99%

“…We describe a new method to deploy an RFID system on a standard commercial honey bee colony that for the first time ensures greater than 90% tag detection accuracy. Our system also solves issues of previous systems affecting bee behavior (reviewed in Odemer, 2022), with the entrance being short, full width, and well ventilated, thus ensuring minimal interference to bee movement, fanning, cleaning, and guarding.…”

Section: Discussionmentioning

confidence: 99%

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Evaluating the foraging performance of individual honey bees in different environments with automated field RFID systems

et al. 2022

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Measuring individual foraging performance of pollinators is crucial to guide environmental policies that aim at enhancing pollinator health and pollination services. Automated systems have been developed to track the activity of individual honey bees, but their deployment is extremely challenging. This has limited the assessment of individual foraging performance in full‐strength bee colonies in the field. Most studies available to date have been constrained to use downsized bee colonies located in urban and suburban areas. Environmental policy‐making, on the other hand, needs a more comprehensive assessment of honey bee performance in a broader range of environments, including in remote agricultural and wild areas. Here, we detail a new autonomous field method to record high‐quality data on the flight ontogeny and foraging performance of honey bees, using radio frequency identification (RFID). We separate bee traffic into returning and exiting tunnels to improve data quality solving many previous limitations of RFID systems caused by traffic jams and the parasitic coupling of RFID antennae. With this method, we assembled a large RFID dataset made of control bee colonies from experiments conducted in different locations and seasons. We hope our results will be a starting point to understand how ontogenetic and environmental factors affect the individual performance of honey bees and that our method will enable large‐scale replication of individual pollinator performance studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Evaluating the foraging performance of individual honey bees in different environments with automated field RFID systems

et al. 2022

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“…The precision of the Bee Tracker exceeds precision values typically found in automated image analysis software (Eikelboom et al, 2019; Gallmann et al, 2020), but reaches values typical for bee counters (Odemer, 2021). The software may, however, only achieve the here reported precision of 96% in experiments with a similar setup, with respect to light conditions during video recording, hues and digits of bee IDs, and the shape, size, and location of the nest cavities in the nesting units.…”

Section: Discussionmentioning

confidence: 83%

Bee Tracker—an open‐source machine learning‐based video analysis software for the assessment of nesting and foraging performance of cavity‐nesting solitary bees

Knauer

Gallmann

Albrecht

2022

Ecology and Evolution

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The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radiofrequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honeybees, behavioral studies on solitary bees still largely depend on direct observations, which is very time‐consuming. Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity‐nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by machine learning‐based software. This Bee Tracker software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees’ thorax and the IDs of their nests according to their positions in the nesting unit. The software is able to identify each nest of each individual nesting bee, which permits to measure individual‐based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters. The software could be adapted to various experimental setups by training it according to a set of videos. The presented method allows to efficiently collect large amounts of data on cavity‐nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi‐field, and field conditions.

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“…Recently there has been an increase in the use of computer vision and deep learning in agriculture (Kamilaris & Prenafeta-Boldú, 2018;Odemer, 2022). This has been prominent in land cover classification (Lu et al, 2017), fruit counting (Afonso et al, 2020), yield estimation (Koirala, Walsh, Wang, & McCarthy, 2019), weed detection (Su, Kong, Qiao, & Sukkarieh, 2021), beneficial and insect pest monitoring (Amarathunga, Grundy, Parry, & Dorin, 2021), and insect tracking and behavioural analysis (Høye et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

“…Automated and detailed pollination monitoring techniques with high functional precision are needed that allow continuous assessment of pollination levels. Mechanised efforts to count insects have been attempted and improved over the last century, although it is only with improved technology and Artificial Intelligence that individual recognition in complex environments has started to emerge as a realistic proposition (Odemer, 2022). In turn, this will facilitate the efficient management of pollinator resources as agriculture increasingly embraces data-driven, AI-enhanced technology (Abdel-Raziq, Palmer, Koenig, Molnar, & Petersen, 2021;Breeze et al, 2021;Howard, Nisal Ratnayake, Dyer, Garcia, & Dorin, 2021).…”

Section: Introductionmentioning

confidence: 99%

Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollination

Ratnayake¹,

Amarathunga²,

Zaman³

et al. 2022

Preprint

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Insects are the most important global pollinator of crops and play a key role in maintaining the sustainability of natural ecosystems. Insect pollination monitoring and management are therefore essential for improving crop production and food security. Computer vision facilitated pollinator monitoring can intensify data collection over what is feasible using manual approaches. The new data it generates may provide a detailed understanding of insect distributions and facilitate fine-grained analysis sufficient to predict their pollination efficacy and underpin precision pollination. Current computer vision facilitated insect tracking in complex outdoor environments is restricted in spatial coverage and often constrained to a single insect species. This limits its relevance to agriculture. Therefore, in this article we introduce a novel system to facilitate markerless data capture for insect counting, insect motion tracking, behaviour analysis and pollination prediction across large agricultural areas. Our system is comprised of Edge Computing multi-point video recording, offline automated multispecies insect counting, tracking and behavioural analysis. We implement and test our system on a commercial berry farm to demonstrate its capabilities. Our system successfully tracked four insect varieties, at nine monitoring stations within a poly-tunnel, obtaining an F-score above 0.8 for each variety. The system enabled calculation of key metrics to assess the relative pollination impact of each insect variety. With this technological advancement, detailed, ongoing data collection for precision pollination becomes achievable. This is important to inform growers and apiarists managing crop pollination, as it allows data-driven decisions to be made to improve food production and food security.

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Approaches, challenges and recent advances in automated bee counting devices: A review

Cited by 36 publications

References 69 publications

Evaluating the foraging performance of individual honey bees in different environments with automated field RFID systems

Evaluating the foraging performance of individual honey bees in different environments with automated field RFID systems

Bee Tracker—an open‐source machine learning‐based video analysis software for the assessment of nesting and foraging performance of cavity‐nesting solitary bees

Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollination

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