Felipe Anderson O. Maciel
Danielo G. Gomes
ABSTRACT
Among the agricultural crops used for human consumption, 75% depends on pollination. As the principal pollinating agent, bees are essential for the food production for humans and the ecosystems sustainability. However, a combination of habitat destruction, climate change and exposure to pesticides and pathogens has led to a significant decrease in bee population. Here we propose a method to recognize status patterns of Apis mellifera colonies through the application of data mining techniques. Using a real dataset from the HiveTool.net containing Apis mellifera temperature, humidity and weight data, we identified 3 status patterns in the observed hive. Our results suggest that the recognized patterns are consistent with a honey bee colony life cycle. Based on the found patterns, we propose a high accuracy classification model capable of automatically identifying colony status for new samples.
- Marcelo A. Aizen and Lawrence D. Harder. 2009. The Global Stock of Domesticated Honey Bees Is Growing Slower Than Agricultural Demand for Pollination. Current Biology 19, 11 (2009), 915--918.Google Scholar
Cross Ref
- Matthias Adolf Becher. 2010. The influence of developmental temperatures on division of labour in honeybee colonies. Ph.D. Dissertation. Halle (Saale), Martin-Luther-Universität Halle-Wittenberg, Diss., 2010.Google Scholar
- Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (2001), 5--32. Google ScholarDigital Library
- Tadeusz Calinski and Jerzy Harabasz. 1974. A dendrite method for cluster analysis. Communications in Statistics 3, 1 (1974), 1--27.Google Scholar
- Thomas Cover and Peter Hart. 1967. Nearest neighbor pattern classification. IEEE transactions on information theory 13, 1 (1967), 21--27. Google ScholarDigital Library
- David J Hand and Keming Yu. 2001. Idiot's Bayes: Not So Stupid after All? International Statistical Review 69, 3 (2001), 385--398.Google Scholar
- Peter Kevan and Truman Phillips. 2001. The economic impacts of pollinator declines: an approach to assessing the consequences. Conservation Ecology 5, 1 (2001).Google Scholar
- Alexandra-Maria Klein, Bernard E Vaissiere, James H Cane, Ingolf Steffan-Dewenter, Saul A Cunningham, Claire Kremen, and Teja Tscharntke. 2007. Importance of pollinators in changing landscapes for world crops. Proceedings of the Royal Society of London B: Biological Sciences 274, 1608 (2007), 303--313.Google ScholarCross Ref
- Douglas Santiago Kridi, Carlos Giovanni Nunes de Carvalho, and Danielo Gonçalves Gomes. 2014. A predictive algorithm for mitigate swarming bees through proactive monitoring via wireless sensor networks. In Proceedings of the 11th ACM symposium on Performance evaluation of wireless ad hoc, sensor, & ubiquitous networks. ACM, 41--47. Google ScholarDigital Library
- Douglas S. Kridi, Carlos Giovanni N. de Carvalho, and Danielo G. Gomes. 2016. Application of wireless sensor networks for beehive monitoring and in-hive thermal patterns detection. Computers and Electronics in Agriculture 127 (2016), 221--235. Google ScholarDigital Library
- Armands Kviesis and Aleksejs Zacepins. 2016. Application of Neural Networks for Honey Bee Colony State Identification. In Carpathian Control Conference (ICCC), 2016 17th International. IEEE, 413--417.Google Scholar
- Armands Kviesis, Aleksejs Zacepins, Mahmut Durgun, and Saban Tekin. 2015. Application of wireless sensor networks in precision apiculture. Engineering for Rural Development (2015), 440--445.Google Scholar
- James MacQueen. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1. Oakland, CA, USA., 281--297.Google Scholar
- W. G. Meikle and Niels Holst. 2015. Application of continuous monitoring of honeybee colonies. Apidologie 46, 1 (2015), 10--22.Google ScholarCross Ref
- Fiona Edwards Murphy, Michele Magno, Pádraig M. Whelan, John O'Halloran, and Emanuel M. Popovici. 2016. b+ WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring. Computers and Electronics in Agriculture 124 (2016), 211--219. Google ScholarDigital Library
- Jeff Ollerton, Rachael Winfree, and Sam Tarrant. 2011. How many flowering plants are pollinated by animals? Oikos 120, 3 (2011), 321--326.Google ScholarCross Ref
- Madeleine M Ostwald, Michael L Smith, and Thomas D Seeley. 2016. The behavioral regulation of thirst, water collection and water storage in honey bee colonies. Journal of Experimental Biology 219, 14 (2016), 2156--2165.Google ScholarCross Ref
- Simon G. Potts, V. L. Imperatriz-Fonseca, H. T. Ngo, J. C. Biesmeijer, Thomas D. Breeze, L. V. Dicks, L. A. Garibaldi, R. Hill, J. Settele, and A. J. Vanbergen. 2016. Summary for policymakers of the assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on pollinators, pollination and food production. Report. Bonn, Germany.Google Scholar
- Zheng-Yan Ruan, Chien-Hao Wang, Hong-Jen Lin, Chien-Peng Huang, Ying-Hao Chen, En-Cheng Yang, Chwan-Lu Tseng, and Joe-Air Jiang. 2017. An Internet of Things-Based Weight Monitoring System for Honey. World Academy of Science, Engineering and Technology, International Journal of Biological, Biomolecular, Agricultural, Food and Biotechnological Engineering 11, 6 (2017), 478--482.Google Scholar
- Víctor Sánchez, Sergio Gil, José M. Flores, Francisco J. Quiles, Manuel A. Ortiz, and Juan J. Luna. 2015. Implementation of an electronic system to monitor the thermoregulatory capacity of honeybee colonies in hives with open-screened bottom boards. Computers and Electronics in Agriculture 119 (2015), 209--216. Google ScholarDigital Library
- Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. 2009. Introdução ao data-mining: mineração de dados. Ciência Moderna.Google Scholar
- Aleksejs Zacepins, Valters Brusbardis, Jurijs Meitalovs, and Egils Stalidzans. 2015. Challenges in the development of Precision Beekeeping. Biosystems Engineering 130 (2015), 60--71.Google ScholarCross Ref
- Aleksejs Zacepins and Toms Karasha. 2012. Web based system for the bee colony remote monitoring. In Application of Information and Communication Technologies (AICT), 2012 6th International Conference on. IEEE, 1--4.Google ScholarCross Ref
- Aleksejs Zacepins, Armands Kviesis, Aldis Pecka, and Vitalijs Osadcuks. 2017. Development of Internet of Things concept for Precision Beekeeping. In Carpathian Control Conference (ICCC), 2017 18th International. IEEE, 23--27.Google ScholarCross Ref
Index Terms
- Data Mining to Characterize Seasonal Patterns of Apis mellifera Honey Bee Colonies
Recommendations
Honey Bee Swarm Cognition: Decision-Making Performance and Adaptation
A synthesis of findings from neuroscience, psychology, and behavioral biology has been recently used to show that several key features of cognition in neuron-based brains of vertebrates are also present in bee-based swarms of honey bees. Here, ...
Honey bee foraging algorithm for multimodal & dynamic optimization problems
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computationWe present a new swarm based algorithm called Honey Bee Foraging (HBF). This algorithm is modeled after the food foraging behavior of the honey bees and performs a swarm based collective foraging for fitness in promising neighborhoods in combination ...
Automated assay and differential model of western honey bee (Apis mellifera) autogrooming using digital image processing
Display Omitted A novel, automated honey bee grooming assay was developed and tested.Five honey bees were coated in baking flour, then their grooming was recorded.Pixel data were thresholded (flour=0, bee=1) and measured over 10 min.Honey bee grooming ...
Comments