AIMC Topic: Bees

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Non-Intrusive System for Honeybee Recognition Based on Audio Signals and Maximum Likelihood Classification by Autoencoder.

Sensors (Basel, Switzerland)
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 ...

Robotic flytrap with an ultra-sensitive 'trichome' and fast-response 'lobes'.

Bioinspiration & biomimetics
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...

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
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...

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.)
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...

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
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...

Varroa Mite Counting Based on Hyperspectral Imaging.

Sensors (Basel, Switzerland)
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...

Tracing the genealogy origin of geographic populations based on genomic variation and deep learning.

Molecular phylogenetics and evolution
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...

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

Sensors (Basel, Switzerland)
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...

Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction.

Journal of hazardous materials
The prediction of ecological toxicity plays an increasingly important role in modern society. However, the existing models often suffer from poor performance and limited predictive capabilities. In this study, we propose a novel approach for ecologic...

Deep learning for identifying bee species from images of wings and pinned specimens.

PloS one
One of the most challenging aspects of bee ecology and conservation is species-level identification, which is costly, time consuming, and requires taxonomic expertise. Recent advances in the application of deep learning and computer vision have shown...