AIMC Topic: Bees

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Bee Together: Joining Bee Audio Datasets for Hive Extrapolation in AI-Based Monitoring.

Sensors (Basel, Switzerland) Sep 19, 2024
Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the li...
Algorithms Animals Artificial Intelligence Bees
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Improving Human Activity Recognition With Wearable Sensors Through BEE: Leveraging Early Exit and Gradient Boosting.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 18, 2024
Early-exiting has recently provided an ideal solution for accelerating activity inference by attaching internal classifiers to deep neural networks. It allows easy activity samples to be predicted at shallower layers, without executing deeper layers,...
Deep Learning Wearable Electronic Devices Machine Learning Pattern Recognition, Automated Bees Algorithms Human Activities Humans Neural Networks, Computer
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Non-Intrusive System for Honeybee Recognition Based on Audio Signals and Maximum Likelihood Classification by Autoencoder.

Sensors (Basel, Switzerland) Aug 21, 2024
Artificial intelligence and Internet of Things are playing an increasingly important role in monitoring beehives. In this paper, we propose a method for automatic recognition of honeybee type by analyzing the sound generated by worker bees and drone ...
Artificial Intelligence Algorithms Animals Neural Networks, Computer Likelihood Functions Signal Processing, Computer-Assisted Bees
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Robotic flytrap with an ultra-sensitive 'trichome' and fast-response 'lobes'.

Bioinspiration & biomimetics Aug 14, 2024
Nature abounds with examples of ultra-sensitive perception and agile body transformation for highly efficient predation as well as extraordinary adaptation to complex environments. Flytraps, as a representative example, could effectively detect the m...
Animals Biomimetics Bees Touch Robotics Flight, Animal Equipment Design
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Investigating the impact of climate variables on the organic honey yield in Turkey using XGBoost machine learning.

Journal of the science of food and agriculture Aug 9, 2024
BACKGROUND: The Turkish organic honey industry, a major player in the global market, faces challenges due to climate fluctuations. Understanding the influence of climate factors on honey production is vital for sustainable farming and economic stabil...
Machine Learning Temperature Climate Change Turkey Humidity Organic Agriculture Beekeeping Honey Wind Bees Animals
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High-resolution proteomics and machine-learning identify protein classifiers of honey made by Sicilian black honeybees (Apis mellifera ssp. sicula).

Food research international (Ottawa, Ont.) Aug 8, 2024
Apis mellifera ssp. sicula, also known as the Sicilian black honeybee, is a Slow Food Presidium that produces honey with outstanding nutraceutical properties, including high antioxidant capacity. In this study, we used high-resolution proteomics to p...
Insect Proteins Animals Proteomics Honey Bees Proteome Machine Learning
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Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Environmental monitoring and assessment Jul 11, 2024
Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Theref...
Neural Networks, Computer Algorithms Environmental Monitoring Animals Bees Groundwater
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Varroa Mite Counting Based on Hyperspectral Imaging.

Sensors (Basel, Switzerland) Jul 9, 2024
Varroa mite infestation poses a severe threat to honeybee colonies globally. This study investigates the feasibility of utilizing the HS-Cam and machine learning techniques for Varroa mite counting. The methodology involves image acquisition, dimensi...
Bees Hyperspectral Imaging Machine Learning Varroidae Algorithms Support Vector Machine Animals Principal Component Analysis Image Processing, Computer-Assisted
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Tracing the genealogy origin of geographic populations based on genomic variation and deep learning.

Molecular phylogenetics and evolution Jul 2, 2024
Assigning a query individual animal or plant to its derived population is a prime task in diverse applications related to organismal genealogy. Such endeavors have conventionally relied on short DNA sequences under a phylogenetic framework. These met...
Genetics, Population Bees Ants Phylogeny Deep Learning Animals Polymorphism, Single Nucleotide Chickens Genomics
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Analysis of Varroa Mite Colony Infestation Level Using New Open Software Based on Deep Learning Techniques.

Sensors (Basel, Switzerland) Jun 13, 2024
Varroa mites, scientifically identified as , 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 s...
Deep Learning Image Processing, Computer-Assisted Bees Beekeeping Software Varroidae Animals Mite Infestations
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