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@article{162024,
        author = {Ankit sharma and Annamayya Katture and Anusha.P.K and Y.R.Sri Lohit and Balarao Anvitha  and Mandala Anvitha and Dr.G.Gifta Jerith and Dr.Thayyaba Khatoon},
        title = {Honey Bee Health Detection using CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {7},
        pages = {238-243},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162024},
        abstract = {This research paper is about honey bees which are more responsible and play an effective role in the environment. Up to one third of the global food production depends on the pollination of honey bees, making them vital. This defines a methodology to create a bee hive health monitoring system through image processing techniques. The health of honey bee populations is a matter of great concern due to their critical role in pollination and ecosystem stability. This presents an innovative approach to honey bee health detection using Machine Learning techniques.
We have gathered a diverse dataset of honey bee-related data, including visual, acoustic, and environmental factors, allowing us to develop a comprehensive health assessment model. Through the application of various machine learning algorithms, we have achieved promising results in accurately classifying the health status of honey bee colonies. Our model can detect common ailments such as Varroa mite infestations, Nosema infections, and overall colony stress levels.
This research represents a valuable tool for beekeepers and conservationists, providing early warning systems for bee colony health and assisting in the preservation of honey bee populations. Furthermore, it offers insights into the broader applications of Machine Learning in monitoring and managing environmental	and	agricultural sustainability.Two databases were used to create models based on Convolutional Neural Network (CNN).The best results consist of 95% accuracy for healthclassification of a bee examples. This work contributes to enhancing text classification techniques, particularly in situations with resource constraints and challenging label acquisition and 82% accuracy in detecting the presence of bees in an image, higher than those found in the state- of-the-art.
},
        keywords = {Honey Bee Detection, Dataset Integration, Machine Learning Algorithms, Support Vector Machines, Swarm Intelligence Algorithms},
        month = {},
        }