Convolutional Neural Network for Honeybee Density Estimation

Honeybees (Apis mellifera L.) perform an important service to the ecosystem as they function as significant pollinators for plants. Over the past few decades, honeybees have suffered a progressive decline. The ability to observe and control the activity and density dynamics of honeybees in their hives in an automated way can allow to stimulate their actions and improve the overall efficiency of the hive. In this paper we present a novel honeybee observation method that is primarily based on honeybee density estimation using a convolutional neural network. First, specially designed stationary robots were positioned inside an arena designed for young honeybees. Three robots were used, each equipped with six infrared sensors for honeybee detection. The hardware and software setup that was used during the raw data collection process is described. Using the collected data from experiments with different numbers of honeybees we tested different convolutional neural networks to evaluate the relation between the network parameters and the estimation accuracy. To obtain better results, the numbers of honeybees were grouped into four different categories. It is shown that the most influential parameters are the number of epochs and the feature map size. By using the correct parameters it is possible to obtain 100 % accuracy during network training process and 86 % accuracy during evaluation process.

[1]  Thomas D. Seeley,et al.  Adaptive significance of the age polyethism schedule in honeybee colonies , 1982, Behavioral Ecology and Sociobiology.

[2]  Serge Kernbach,et al.  Get in touch: cooperative decision making based on robot-to-robot collisions , 2009, Autonomous Agents and Multi-Agent Systems.

[3]  T. Seeley,et al.  Group decision making in swarms of honey bees , 1999, Behavioral Ecology and Sociobiology.

[4]  Tucker R. Balch,et al.  Representing Honey Bee Behavior for Recognition Using Human Trainable Models , 2004, Adapt. Behav..

[5]  Stjepan Bogdan,et al.  A robotic system for researching social integration in honeybees , 2017, PloS one.

[6]  Thomas Schmickl,et al.  Dynamics of Collective Decision Making of Honeybees in Complex Temperature Fields , 2013, PloS one.

[7]  F. Ratnieks,et al.  Worker allocation in insect societies: coordination of nectar foragers and nectar receivers in honey bee (Apis mellifera) colonies , 1999, Behavioral Ecology and Sociobiology.

[8]  Serge Kernbach,et al.  ASSISI: Charged Hot Bees Shakin' in the Spotlight , 2013, 2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems.

[9]  Leonardo Franco,et al.  Neural Network Architecture Selection: Can Function Complexity Help? , 2009, Neural Processing Letters.

[10]  François Chollet,et al.  Keras: The Python Deep Learning library , 2018 .

[11]  T. Seeley.  The Wisdom of the Hive , 1995 .

[12]  Dervis Karaboga,et al.  A survey: algorithms simulating bee swarm intelligence , 2009, Artificial Intelligence Review.

[13]  Scott Camazine,et al.  The mystery of swarming honeybees: from individual behaviors to collective decisions. , 1999 .

[14]  Dong Ling Tong,et al.  Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection , 2010, Int. J. Mach. Learn. Cybern..

[15]  F Mondada,et al.  Social Integration of Robots into Groups of Cockroaches to Control Self-Organized Choices , 2007, Science.

[16]  Carl Anderson.  Simulation of the Feedbacks and Regulation of Recruitment Dancing in Honey Bees , 1998, Adv. Complex Syst..

[17]  Kyandoghere Kyamakya,et al.  CNN based high performance computing for real time image processing on GPU , 2011, Proceedings of the Joint INDS'11 & ISTET'11.

[18]  Karl Crailsheim,et al.  Endothermic heat production in honeybee winter clusters , 2003, Journal of Experimental Biology.

[19]  James D. Ellis,et al.  Colony losses, managed colony population decline, and Colony Collapse Disorder in the United States , 2010 .

[20]  Francesco Mondada,et al.  How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model , 2018, Living Machines.

[21]  J. Biesmeijer,et al.  Modelling collective foraging by means of individual behaviour rules in honey-bees , 1998, Behavioral Ecology and Sociobiology.

[22]  Damjan Miklic,et al.  Combined actuator sensor unit for interaction with honeybees , 2015, 2015 IEEE Sensors Applications Symposium (SAS).

[23]  Scott Camazine,et al.  Self-Organized Thermoregulation of Honeybee Clusters , 1995 .

[24]  S. Camazine,et al.  A model of collective nectar source selection by honey bees , 1991 .

[25]  Thomas Schmickl,et al.  Embodiment of Honeybee's Thermotaxis in a Mobile Robot Swarm , 2009, ECAL.

[26]  Thomas Schmickl,et al.  Estimation of moving agents density in 2D space based on LSTM neural network , 2017, 2017 Evolving and Adaptive Intelligent Systems (EAIS).

[27]  Thomas Schmickl,et al.  The efficiency of the RULES-4 classification learning algorithm in predicting the density of agents , 2014 .

[28]  Serge Kernbach,et al.  ASSISI: Mixing Animals with Robots in a Hybrid Society , 2013, Living Machines.

[29]  Per Kryger,et al.  Self-organization of circadian rhythms in groups of honeybees (Apis mellifera L.) , 1994, Behavioral Ecology and Sociobiology.