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Found 8 papers, 2 papers with code
Varroa destructor detection on honey bees using hyperspectral imagery
Hyperspectral (HS) imagery in agriculture is becoming increasingly common.
Raspberry Pi Bee Health Monitoring Device
A declining honeybee population could pose a threat to a food resources of the whole world one of the latest trend in beekeeping is an effort to monitor a health of the honeybees using various sensors and devices.
Towards Phytoplankton Parasite Detection Using Autoencoders
Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples.
Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A survey
Wide use and availability of the machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains.
SIFT and SURF based feature extraction for the anomaly detection
In this paper, we suggest a way, how to use SIFT and SURF algorithms to extract the image features for anomaly detection.
Feature space reduction as data preprocessing for the anomaly detection
We show that the convolutional autoencoder architecture doesn't have a significant effect for this task and we prove the potential of our approach on the real world dataset.
Visual diagnosis of the Varroa destructor parasitic mite in honeybees using object detector techniques
Here we present an object detector based method for health state monitoring of bee colonies.
Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection
Experiments show that both algorithms are applicable for cases with low feature space dimensions but the learning-based method produces less exploitable strategies and it is scalable to higher dimensions.
