Architecture of automatized control system for honey bee indoor wintering process monitoring and control Aleksejs Zacepins, Egils Stalidzans Department of Computer Systems Faculty of Information Technologies Latvia University of Agriculture Jelgava, Latvia E-mail:
[email protected];
[email protected] Abstract—Honey bee wintering process is one of the biological processes, which nowadays can be monitored and controlled using the information technologies, control methods and tools. Control and monitoring of such process is really complicated and not a trivial task, because behavior of the biological system, biological objects and their reaction to human intervention is unpredictable. To complete this task it is necessary to build complex control system architecture with many subsystems and elements. The automatized system architecture proposed by the authors is developed to improve the honey bee wintering process and to incorporate information technologies into this process. The system is mainly used for individual honey bee colony monitoring and control. In order to develop such biological process control system it is necessary to use various information technologies and expert knowledge in the specific biological field. Honey bee wintering process takes place in a closed environment, where microclimate control system controls the environment microclimate parameters based on measurement device data. At the same time honey bee hive temperature measurement system collects data about bee activity. Bee hive temperature is the main factor, which is used for bee activity monitoring. Measurement results are automatically transferred to the remote computational system, which completes data summarizing and analyzing. Based on predefined algorithms and prediction models, computational system recognizes the state of the bee colony. Taking into account additional external data and taking into account operator’s judgment, the system makes decision whether to change the main control task. The automatized system architecture for the complex task of honeybee wintering process monitoring and control proposed by the authors can be divided into smaller logical and technical parts or so called sub-systems. These sub-systems can be developed individually and independently, but by combining them all together it is possible to build an advanced control system. The authors also consider that important place in the system architecture is taken by prediction models. The usage of the prediction models in the architecture allows the system to make a decision about bee family state more precisely and independently from the operator. During the honey bee wintering process it is possible to use quality and quantitative models. Mainly models are used for determination of the activity stage of the bee colony. Keywords-control system architecture; honey bee wintering process; control and monitoring task;
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I.
INTRODUCTION
Nowadays automatized and automatic control solutions can be applied not only in industrial manufacturing, but also for biological system control and monitoring tasks. While controlling biological process it is important not only to grant stable and convenient environmental parameters, but also to monitor individual parameters, activity and behavior of the biological object. Such parameters can be soil temperature and humidity, color of the leaves or bee family temperature. Today, when it is possible to apply modern information technologies and tools, biological process can be researched more precisely an in more detail. Biological system is a set of the biological elements, which mutually interacting and forms the whole object to perform specific function. Biological systems interacts with environment and others systems. Examples of the biological systems are: cell, organ, organisms, population, ecosystem and others. There are also many specific properties of the biological system: [definition from http://www.avifarm.ru]: •
Biosystem performs specific function (biochemical, physiological or other);
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Biosystem sum of properties is not equal to the sum of all system elements;
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Biosystem consist of the subsystems;
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Biosystem is continuously changing;
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Biosystem can adapt to the changes of the environment;
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Biosystem can grow and reproduce itself.
It is known, that biosystem has self-regulation property, which means, that it can be adapted and can be reorganized based on external factors, to save its optimal functioning level [1]. Bee wintering process is one of the biological processes, which nowadays can be monitored and controlled using the information, control and monitoring technologies and tools. Control and monitoring of such process is a really complicated and not a trivial task, because the behavior of a biological
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system and its reaction to human intervention is unpredictable. To complete this task it is needed to build complex control system architecture with many subsystems and elements. Today there are developed many systems, which perform only a single defined task, for example only microclimate control in closed environment or only object parameter measurements [2-5]. But this paper authors offers unified system architecture for honey bee wintering process control combining environmental microclimate control with individual biological object monitoring. II.
Figure 1. Honey bee wintering process environment
PROPOSED SYSTEM ARCHITECTURE
Automatic control system architecture – is control system abstract model, where system components and its interactions are described. Architecture elements are related one with another and they compose whole automatic system, which grants solution for the specific task on the architecture level. At the same time system architecture leaves enough freedom, while choosing concrete technical solutions and it is not tied to the only one solution. It is possible to develop many control systems with different hardware solutions and system elements based on one system architecture [6], [7]. As system architecture elements are sensors, control devices, measurement converters, PLC, computers, interfaces, protocols, industrial networks etc. While developing system architecture it is necessary to anticipate many future automatic system properties [6]: •
It should be weak dependency of the system architecture elements;
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It should be able to test the system;
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It should be able to find erroneous system parts;
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It should be able to fix the system within minimal resources;
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System should be reliable;
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It should be able to easily maintain the system;
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System should be secure;
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It should be able to reconfigure the system to adapt to the process changes.
In this paper authors propose architecture of automatized control system for honeybee indoor wintering process monitoring and control. One of the main features of this biological process is that it usually takes place without direct human participation and intervention. The main aspect is that environment microclimate parameters (mainly air temperature in case of honey bee wintering process) affect the activity state and behavior of honey bee colony (Fig. 1). Developed whole system architecture for honey bee wintering process control and monitoring is demonstrated in Fig. 2.
Figure 2. Control system architecture for honey bee indoor wintering process monitoring and control
Honeybee wintering process takes place in a closed environment, where microclimate control system controls the environment microclimate parameters based on measurement device data. As the main microclimate parameter is air temperature, but additionally air humidity, air gas content can be measured [8]. The control system can be developed and implemented using specific controlling devices, sensors and actuators. At the same time bioobject parameters measurement system collects data about bioobject activity. Bee colony temperature is the main factor, which is used for bee activity monitoring. Measurement results are automatically transferred to the remote computational system, which completes its summarizing and analyzing [9]. The measurement or data acquisition system can be developed using sensor network architecture. Based on predefined algorithms and prediction models, computational system recognizes the state of the biological object. Taking into account additional external data and attracting the operator, system makes the decision to change the control task. The main objective of the computational system is to save and demonstrate received biological object data. One of the ways to effectively publish data is to develop and utilize a web based application. Based on author achieved bee colonies temperature measurement results during the passive wintering period, while bees are kept in the specific wintering building, it can be concluded that it is possible to precisely find out the date, when the bee colony died and find out the dates of the potential start of the brood rearing process [10].
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Authors developed technical solution based on proposed system architecture with specific elements and devices is demonstrated in Fig. 3.
Figure 3. Solution based on proposed system architecture with specific elements and devices for honeybee indoor wintering process monitoring and control
The authors consider that important place in the system architecture is taken by prediction models. Based on those models decision could be more precise and qualitative.
Mainly models are used for the prediction of the state of the biological system and for the determination of the stage of biosystem development.
Task of the models is to represent various real physical, biological, economical or other processes. Usually models gives a simplified picture about process, but nevertheless information, which is provided by the model, is useful for the researcher [11].
The authors developed system is fully operating for two years granting the optimal wintering conditions for bee colonies. This winter (season from December 2011 till April 2012) twenty bee hives are placed into the wintering building for practical observations and temperature monitoring (Fig. 4). Bee hives are separated into two main groups: hives with heating isolation and without heating isolation. Each group is further separated based on the bee colony size.
Control system can automatically make a decision about the state of the biological object or object activity and can recommend needed actions or complete them independently from the operator, based on those two model types: quality models and quantity models, which are basically developed on the basis of expert knowledge or taking into account the previous experiment results. During the honeybee wintering process it is possible to use three quality models: •
Model to determine honeybee family developmental stage in response to beehive temperature changes;
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Model to determine the possibility of the preswarming in depend on bee count in the hive;
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Model to conclude about the bee family food consumption in depend on beehive temperature changes.
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Figure 4. Bee hives in the wintering building
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REFERENCES
CONCLUSION
A complex task of honey bee indoor wintering process monitoring and control can be done by combining automatic control system benefits with data mining and data analyzing technologies. The authors’ proposed automatized system architecture is developed to improve the honey bee wintering process. System is used for controlling and individual monitoring of honey bee colonies in a bee wintering building with controlled microclimate environment. In order to develop such biological control system it is needed to use various information technologies and expert knowledge in specific biological field. Based on the authors proposed automatized system architecture the complex task of honeybee wintering process monitoring and control can be divided into smaller logical and technical parts or so called sub-systems. These sub-systems can be developed individually and independently, but by combining them all together it is possible to build an advanced automatized control system. The offered system architecture and described approach could be also applied in other various fields, where biological object is the center element. For example, similar systems could be built for monitoring the vegetables in greenhouses, grains in dryers, and etc. The use of the prediction models in the architecture allows the system to make a decision about bioobject’s state more precisely and independently from the operator. ACKNOWLEDGMENT Academic study and publication financed by the project „Support for doctoral studies in LUA” / 2009/0180/1DP/1.1.2.1.2/09/IPIA/VIAA/017/ agreement Nr. 04.4-08/EF2. D1.09.
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A.B. Rubin, N.F. Pitjeva, and G.J. Riznichenko, Kinetics of the biological processes (Кинетика биологических процессов). MGU: 1987. [2] V. Ogorodnik, J. Kleperis, A. Kristinsh, I. Gvardyna, A. Cesnieks, and A. Vilde, „Automated control of the grain drying process,” Proceedings of the Scientific International Conference “Engineering for rural development,” 2009, pp. 324-327. [3] G.D. Pasgianos, K.G. Arvanitis, P. Polycarpou, and N. Sigrimis, „A nonlinear feedback technique for greenhouse environmental control,” Computers and Electronics in Agriculture, vol. 40, October 2003, pp. 153-177. [4] K.G. Arvanitis, P.N. Paraskevopoulos, and A.A. Vernardos, „Multirate adaptive temperature control of greenhouses,” Computers and Electronics in Agriculture, vol. 26, Issue 3, May 2000, pp. 303-320. [5] D. Stipanicev, and D. Marasovic, „Networked embedded greenhouse monitoring and control,” Proceedings of the Scientific International Conference „IEEE Conference on Control Applications,” 2003, pp.1350-1355. [6] J. Klir, Systemotology. Automation solutions of system tasks (Системология. Автоматизация решения системных задач). Radio and Coomunication: 1990. [7] V.V. Denisenko, Computer control of technological process (Компьютерное управление технологическим процессом). Горячая линия-Телеком, 2009. [8] A. Zacepins, J. Meitalovs, and E. Stalidzans, “Model based real time automated temperature control system for risk minimization in honey bee wintering building,” Proceedings of the Scientific International Conference „8th International industrial simulation conference 2010”, 2010, pp. 245-247. [9] A. Zacepins, J. Meitalovs, V. Komasilovs, and E. Stalidzans, „Temperature sensor network for prediction of possible start of brood rearing by indoor wintered honey bees,” Proceedings of the Scientific International Conference „ICCC 2011”, 2011, pp. 469-472. [10] A. Zacepins, „Application of bee hive temperature measurements for recognition of bee colony state,” Proceedings of the 5-th International Scientific Conference „AICT 2012”, 2012. Accepted for publication. [11] J.A. Sokolowski, and C.M. Banks, Principles of Modeling and Simulation. NJ: Wiley: 2009.
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