The development of honey bee colonies assessed using a new semi-automated brood counting method: CombCount

Colin, Théotime; Bruce, Jake; Meikle, William G.; Barron, Andrew B.

doi:10.1371/journal.pone.0205816

Cited by 33 publications

(23 citation statements)

References 42 publications

(42 reference statements)

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“…Suggestions by the ICP-PR Bee Protection Group resulted in improvements of the method; however, sublethal effects were not focused on [ 59 ]. With current technical advances, it is possible to enhance the selection process of study colonies, digitize colony and brood assessments, and monitor the flight activity to display sublethal effects more accurately [ 57 , 60 , 61 ]. This refinement could be highly relevant for the further improvement of regulatory relevant methodologies in order to identify sublethal effects at the colony level and help render the present terminology more precise.…”

Section: Discussionmentioning

confidence: 99%

Chronic High Glyphosate Exposure Delays Individual Worker Bee (Apis mellifera L.) Development under Field Conditions

Odemer

Alkassab

Bischoff

et al. 2020

Insects

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The ongoing debate about glyphosate-based herbicides (GBH) and their implications for beneficial arthropods gives rise to controversy. This research was carried out to cover possible sublethal GBH effects on the brood and colony development, adult survival, and overwintering success of honey bees (Apis mellifera L.) under field conditions. Residues in bee relevant matrices, such as nectar, pollen, and plants, were additionally measured. To address these questions, we adopted four independent study approaches. For brood effects and survival, we orally exposed mini-hives housed in the “Kieler mating-nuc” system to sublethal concentrations of 4.8 mg glyphosate/kg (T1, low) and 137.6 mg glyphosate/kg (T2, high) over a period of one brood cycle (21 days). Brood development and colony conditions were assessed after a modified OECD method (No. 75). For adult survival, we weighed and labeled freshly emerged workers from control and exposed colonies and introduced them into non-contaminated mini-hives to monitor their life span for 25 consecutive days. The results from these experiments showed a trivial effect of GBH on colony conditions and the survival of individual workers, even though the hatching weight was reduced in T2. The brood termination rate (BTR) in the T2 treatment, however, was more than doubled (49.84%) when compared to the control (22.11%) or T1 (20.69%). This was surprising as T2 colonies gained similar weight and similar numbers of bees per colony compared to the control, indicating an equal performance. Obviously, the brood development in T2 was not “terminated” as expected by the OECD method terminology, but rather “slowed down” for an unknown period of time. In light of these findings, we suggest that chronic high GBH exposure is capable of significantly delaying worker brood development, while no further detrimental effects seem to appear at the colony level. Against this background, we discuss additional results and possible consequences of GBH for honey bee health.

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Section: Discussionmentioning

confidence: 99%

Chronic High Glyphosate Exposure Delays Individual Worker Bee (Apis mellifera L.) Development under Field Conditions

Odemer

Alkassab

Bischoff

et al. 2020

Insects

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“…The area of sealed brood per frame was estimated later from the frame photographs using either ImageJ software (version 1.47. W. Rasband, National Institutes of Health, USA), or CombCount; 38 this method has been described in other publications (see 8,13,16,25 ). Any colony that had capped brood was included in the study.…”

Section: Methodsmentioning

confidence: 99%

Landscape factors influencing honey bee colony behavior in Southern California commercial apiaries

Meikle

Weiss

Beren

2020

Sci Rep

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Colony brood levels, frames of bees (adult bee mass) and internal hive temperature were monitored for 60 colonies for each of two years as they were moved from agricultural, tree crop and mountain landscapes in southern California to blueberry and almond pollination sites. Hive weight was also continuously monitored for 20 of those hives for 6 weeks for both years, during commercial pollination. Pesticide residues in wax, honey and beebread samples were analyzed by composite apiary samples. While colonies in mountain sites had more adult bees and brood than those in agricultural sites in August, by October brood levels were higher in colonies from agricultural sites. Though hives from different original landscapes differed in size in October, hive assessments revealed no differences between the groups after co-wintering when graded for commercial almond pollination. Beebread from hives in agricultural sites had greater agrochemical diversity and in general higher pesticide hazard quotients than those from mountain sites, but those hives also had higher and more constant temperatures from September until January than hives from mountain sites. Hives placed in commercial almond pollination gained on average 287 g per d, compared to an average loss of 68 g per d for colonies in commercial blueberry pollination, although weight data indicated greater foraging effort by colonies in blueberries, possibly due to the proximity and abundance of almond pollen during bloom. Temperature monitoring was effective at distinguishing hive groups and had the best overall value in terms of equipment, installation, colony disturbance and information yield.

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“…More recently, Colin et al (2018) developed the software Comb-Count to assess capped brood and capped honey. Although the software is able to detect both classes, it requires a user to distinguish the contents using selection tools.…”

Section: Related Workmentioning

confidence: 99%

Automatic detection and classification of honey bee comb cells using deep learning

Alves

Pinto

Ventura

et al. 2020

Computers and Electronics in Agriculture

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In a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3; MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed to be the best compromise between training cost, with~9 s for processing all cells in a comb image, and accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBeesource).

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The development of honey bee colonies assessed using a new semi-automated brood counting method: CombCount

Cited by 33 publications

References 42 publications

Chronic High Glyphosate Exposure Delays Individual Worker Bee (Apis mellifera L.) Development under Field Conditions

Chronic High Glyphosate Exposure Delays Individual Worker Bee (Apis mellifera L.) Development under Field Conditions

Landscape factors influencing honey bee colony behavior in Southern California commercial apiaries

Automatic detection and classification of honey bee comb cells using deep learning

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