AIMC Topic: Bees

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Deep Learning-Driven Discovery of Bee-Safe Isoxazoline Pesticide Candidates.

Journal of agricultural and food chemistry
Isoxazoline pesticides, such as fluxametamide, while effective against parasites and pests, pose a severe environmental threat due to their high toxicity to honeybees - critical pollinators essential for ecosystem health and food security. Existing p...

Classification of images of bee pollen according to their producers.

PloS one
The food industry is witnessing a growing interest in pollen due to its nutritional and energy composition. Consumers of bee pollen are increasingly eager to learn about the origins of the products they purchase. Establishing the geographical origin ...

Flight and Floral Acoustic Signals for Bee Species Identification.

Neotropical entomology
Animal identification is pivotal for ecological studies, yet automated recognition tools for bee species remain underexplored. Here, we present a machine learning approach using a Random Forest algorithm to identify five bee species representing thre...

Integrative Omics and AI-Driven Systems Biology: Multilayer Networks Decoding Health and Resilience.

Journal of proteome research
Honey bees () are vital pollinators essential for maintaining ecosystem stability and global food production, but they face escalating threats from pathogens, agrochemicals, and climate change. Although proteomics has advanced our understanding of be...

Image-based honey bee larval viral and bacterial diagnosis using machine learning.

Scientific reports
Honey bees are essential pollinators of ecosystems and agriculture worldwide. With an estimated 50-80% of crops pollinated by honey bees, they generate approximately $20 billion annually in market value in the U.S. alone. However, commercial beekeepe...

Hidden Markov model for acoustic pesticide exposure detection and hive identification in stingless bees.

PloS one
Pollinator populations are declining globally at an unprecedented rate, driven by factors such as pathogens, habitat loss, climate change, and the widespread application of pesticides. Although colony losses remain difficult to prevent, precision bee...

Neuronal correlates of sleep in honey bees.

Neural networks : the official journal of the International Neural Network Society
Honey bees Apis mellifera follow the day-night cycle for their foraging activity, entering rest periods during darkness. Despite considerable research on sleep behaviour in bees, its underlying neurophysiological mechanisms are not well understood, p...

Honeybee colony soundscapes: Decoding distance-based cues and environmental stressors.

Ecotoxicology and environmental safety
Honey bees play a crucial role in agricultural productivity and ecological stability, yet their interactions with environmental stressors, particularly volatile organic compounds (VOCs) and pollutants, pose significant challenges to their cognitive f...

Identification and adulteration detection of Heterotrigona itama and Apis dorsata honey using differential scanning calorimetry and convolutional neural networks with data augmentation.

Food chemistry
This study presents a simple approach for detecting honey adulteration by integrating calorimetric data from differential scanning calorimetry (DSC) with machine learning classification (MLC) techniques, specifically using convolutional neural networ...

Digital image processing combined with machine learning: A novel approach for bee pollen classification.

Food research international (Ottawa, Ont.)
The classification of bee pollen is crucial for ensuring product authenticity, quality control, and fraud prevention, particularly given the high commercial value of stingless bee pot-pollen. Although traditional pollen analysis methods are available...