Automatic monitoring of honeybees’ activity outside of the hive from UHD video

Avramović, Aleksej; Jovanovic, Vedran; Pilipović, Ratko; Stojnić, Vladan; Risojević, Vladimir; Gajić, Slavica; Simić, Mitar; Sevo, Igor; Muštra, Mario; Babić, Zdenka; Filipi, Janja

doi:10.1109/neurel.2018.8587026

Cited by 2 publications

(5 citation statements)

References 7 publications

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“…Fig. 6 Obtained results for channels at the entrance to the hive However, the inverse behavior was registered at the end of the day-relative humidity was increasing while temperature and bee activities were decreasing.…”

Section: Resultsmentioning

confidence: 92%

“…In the active method we monitor bees over a minefield and find the most frequently visited places, which then could be marked as suspected mine-contaminated areas. Our approach is based on an Unmanned Aerial Vehicle (UAV), high definition georeferenced video capturing images in both the visible and infrared spectrum, and sophisticated video analysis algorithms [5][6]. The passive method is based on the fact that during foraging honeybees are collecting particles and odors from surrounding.…”

Section: Introductionmentioning

confidence: 99%

“…We are using bees applied to free fly on test mine fields in order to collect TNT and DNT particles which they are accumulating in the hive. The main advantages of using bees include the short period required for conditioning, the ability to cover a wide area in large numbers, and using honeybees for explosive detection is safer and cheaper additional method to the conventional methods [5].…”

Section: Introductionmentioning

confidence: 99%

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Honeybee Activity Monitoring in a Biohybrid System for Explosives Detection

et al. 2019

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Free-flying honeybees can electrostatically collect particles from air in the flying and foraging areas, which in conjunction with organic-based explosive vapor sensing films, placed at the entrance to the beehive, can be used as a passive explosive sensing mechanism. Moreover, bees can be trained to actively search for a smell of explosive. Using trained honeybees in conjunction with a system for honeybee localization enables generation of a spatial-time honeybee density map, where the most visited places point to suspicious areas. In both methods (passive and active), bees' activity monitoring plays a significant role, providing information about environmental parameters and activities of bees at the entrance and exit of a beehive. In this paper we present the design and realization of an electronic system for bee activity monitoring at the front of a hive while using bees for explosive detection. The system also monitors air temperature and relative humidity. Results obtained to date from activity monitoring are useful in planning testing activities within our active and passive method, as it can determine the optimal period of the day and environmental parameters in which bees are most active.

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Section: Resultsmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Honeybee Activity Monitoring in a Biohybrid System for Explosives Detection

et al. 2019

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“…Despite recent advances, the detection of small, fast-moving objects, such as honeybees, where processing speed plays a critical role, remains a challenging task. In [ 7 , 8 , 9 , 10 , 11 ], several systems for identifying honeybees and pollen loads, as well as monitoring the health conditions in a beehive were proposed. Babic et al [ 7 ] proposed a Raspberry Pi-based system for the detection of pollen-bearing bees.…”

Section: Introductionmentioning

confidence: 99%

“…Babic et al [ 7 ] proposed a Raspberry Pi-based system for the detection of pollen-bearing bees. The authors in [ 8 , 10 ] proposed a method for automatic monitoring of honeybees’ activity outside of the hive using a video captured by unmanned aerial vehicles. The study [ 9 ] presented a portable computer vision system that could monitor the infestation level of the Varroa destructor mite in a beehive by recording a video sequence of honeybees.…”

Section: Introductionmentioning

confidence: 99%

An Approximate GEMM Unit for Energy-Efficient Object Detection

Pilipović

Risojević

Božič

et al. 2021

Sensors

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Edge computing brings artificial intelligence algorithms and graphics processing units closer to data sources, making autonomy and energy-efficient processing vital for their design. Approximate computing has emerged as a popular strategy for energy-efficient circuit design, where the challenge is to achieve the best tradeoff between design efficiency and accuracy. The essential operation in artificial intelligence algorithms is the general matrix multiplication (GEMM) operation comprised of matrix multiplication and accumulation. This paper presents an approximate general matrix multiplication (AGEMM) unit that employs approximate multipliers to perform matrix–matrix operations on four-by-four matrices given in sixteen-bit signed fixed-point format. The synthesis of the proposed AGEMM unit to the 45 nm Nangate Open Cell Library revealed that it consumed only up to 36% of the area and 25% of the energy required by the exact general matrix multiplication unit. The AGEMM unit is ideally suited to convolutional neural networks, which can adapt to the error induced in the computation. We evaluated the AGEMM units’ usability for honeybee detection with the YOLOv4-tiny convolutional neural network. The results implied that we can deploy the AGEMM units in convolutional neural networks without noticeable performance degradation. Moreover, the AGEMM unit’s employment can lead to more area- and energy-efficient convolutional neural network processing, which in turn could prolong sensors’ and edge nodes’ autonomy.

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Automatic monitoring of honeybees’ activity outside of the hive from UHD video

Cited by 2 publications

References 7 publications

Honeybee Activity Monitoring in a Biohybrid System for Explosives Detection

Honeybee Activity Monitoring in a Biohybrid System for Explosives Detection

An Approximate GEMM Unit for Energy-Efficient Object Detection

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