—Monitoring the pollen foraging behavior of honey-bees is an important task that is beneficial to beekeepers, allowing them to understand the health status of their honeybee colonies. To perform this task, monitoring systems should have the ability to automatically recognize images of pollen-bearing honeybees extracted from videos recorded at the beehive entrance. In this paper, a novel convolutional neural network architecture is proposed for recognizing pollen-bearing and non-pollen-bearing honeybees from their images. The performance of the proposed model is illustrated based on a real dataset and the obtained results show that it performs better than some other state-of-the-art deep learning architectures like VGG16, VGG19, or Resnet50 in terms of both accuracy and execution time. Thus, the proposed model can be considered an effective algorithm for designing automatic honeybee colony monitoring systems.

A Novel Convolutional Neural Network Architecture for Pollen-Bearing Honeybee Recognition

Varroa mites, scientifically identified as Varroa destructor, pose a significant threat to beekeeping and cause one of the most destructive diseases affecting honey bee populations. These parasites attach to bees, feeding on their fat tissue, weakening their immune systems, reducing their lifespans, and even causing colony collapse. They also feed during the pre-imaginal stages of the honey bee in brood cells. Given the critical role of honey bees in pollination and the global food supply, controlling Varroa mites is imperative. One of the most common methods used to evaluate the level of Varroa mite infestation in a bee colony is to count all the mites that fall onto sticky boards placed at the bottom of a colony. However, this is usually a manual process that takes a considerable amount of time. This work proposes a deep learning approach for locating and counting Varroa mites using images of the sticky boards taken by smartphone cameras. To this end, a new realistic dataset has been built: it includes images containing numerous artifacts and blurred parts, which makes the task challenging. After testing various architectures (mainly based on two-stage detectors with feature pyramid networks), combination of hyperparameters and some image enhancement techniques, we have obtained a system that achieves a mean average precision (mAP) metric of 0.9073 on the validation set. 

Analysis of Varroa Mite Colony Infestation Level Using New Open Software Based on Deep Learning Techniques

The decline in global bee populations significantly threatens biodiversity and food production. Monitoring beehives are crucial for understanding colony health and behavior. In this paper, we propose an analysis of different beekeeping approaches by leveraging deep neural network techniques to analyze the buzzing activity within beehives across multiple greenhouse environments. Our focus is identifying the bee’s presence or absence through audio analysis. This is crucial for early detection of hive issues, optimizing hive management, and public awareness efforts to protect bees and ecosystem sustainability. The first step involves spectrogram analysis of the buzzing sounds recorded within the hives. This study employed three distinct deep learning models: DenseNet121, ResNet50, and InceptionV3. Experimental evaluation using the Open Source Beehive and NU-Hive datasets demonstrates the superiority of InceptionV3 with an accuracy of approximately 72%, outperforming the other models. We evaluated a distributed approach with the VGG16 model, achieving an accuracy of 65%. Our findings emphasize the importance of selecting appropriate deep learning architectures for bee species classification tasks. Moreover, the practical implications of our approach extend beyond identifying bees’ species, as it can be applied to monitor hive health, disease detection, and stress levels within colonies. Our work is in the direction of knowing the transition requirements between a centralized approach and federated learning with the support of the arrival of RedCap devices and 5G networks capable of offering ad-hoc slices for each service.

Spectrogram Based Bee Sound Analysis with DNNs: a step toward Federated Learning approach

: Machine learning and computer vision are dynamically growing fields, which have proven to be able to solve very complex tasks. They could also be used for the monitoring of the honeybee colonies and for the inspection of their health state, which could identify potentially dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. In this paper, we present an overview of the state-of-the-art computer vision and machine learning applications used for bee monitoring. We also demonstrate the potential of those methods as an example of an automated bee counter algorithm. The paper is aimed at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce to them its possibilities, therefore each family of applications is opened by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use the machine learning techniques for other applications in bee monitoring.

Machine Learning and Computer Vision Techniques in Bee Monitoring Applications

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Machine Learning and Computer Vision Techniques in Bee Monitoring Applications