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Parche de flores autónomo: Sistema computacional para la investigación experimental con abejas melíferas
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Abstract
En investigaciones sobre el comportamiento de las abejas melíferas que interactúan con parches florales artificiales, se ha determinado que las tareas manuales vinculadas al suministro, observación y reposición de la solución de sacarosa son procesos que demandan un considerable tiempo y recursos. Con el fin de superar esta limitación, se desarrolló un sistema computarizado que integra un parche autónomo con sensores capaces de monitorear la dinámica del néctar en cada flor. El diseño replicable de cada uno de estos sensores se basa en un circuito 555 en configuración astable acoplado a un Arduino para estimar y cuantificar el néctar. Los ensayos de campo validaron la viabilidad del sensor, confirmando su capacidad para estimar la presencia de néctar aplicando el modelo de Avrami a la señal obtenida. Posteriormente, se llevó a cabo una simulación en Python con el objetivo de identificar los parámetros mínimos requeridos para la construcción y optimización del sistema. En esta fase, se identificaron las variables configurables del parche floral que presentan mayor influencia en la eficiencia global. Para ello, se adoptó un enfoque basado en agentes, expandiendo una simulación existente sobre el forrajeo y la danza del meneo con nuevos agentes que representan las flores y el suministro autónomo. Analizando métricas de eficiencia —como néctar recolectado, tiempos de reposición y visitas a flores vacías— se definió la configuración óptima para la construcción del parche floral y su uso en experimentos bajo condiciones naturales. Esto nos permite conocer la interacción de las abejas con la naturaleza y nos ayuda a encontrar mejores maneras de cuidarlas y asegurar el bienestar de los ecosistemas.
Research on the behavior of honeybees interacting with artificial flower patches has determined that manual tasks related to supplying, observing, and replenishing the sucrose solution are processes that require considerable time and resources. To overcome this limitation, a computerized system was developed that integrates an autonomous patch with sensors capable of monitoring the dynamics of nectar in each flower. The replicable design of each of these sensors is based on a 555 circuit in an astable configuration coupled to an Arduino to estimate and quantify the nectar. Field trials validated the viability of the sensor, confirming its ability to estimate the presence of nectar by applying the Avrami model to the signal obtained. Subsequently, a simulation was carried out in Python with the aim of identifying the minimum parameters required for the construction and optimization of the system. In this phase, the configurable variables of the floral patch that have the greatest influence on overall efficiency were identified. To do this, an agent-based approach was adopted, expanding an existing simulation on foraging and waggle dancing with new agents representing flowers and autonomous supply. By analyzing efficiency metrics—such as nectar collected, replenishment times, and visits to empty flowers—the optimal configuration for the construction of the floral patch and its use in experiments under natural conditions was defined. This allows us to understand the interaction of bees with nature and helps us find better ways to care for them and ensure the well-being of ecosystems.
Research on the behavior of honeybees interacting with artificial flower patches has determined that manual tasks related to supplying, observing, and replenishing the sucrose solution are processes that require considerable time and resources. To overcome this limitation, a computerized system was developed that integrates an autonomous patch with sensors capable of monitoring the dynamics of nectar in each flower. The replicable design of each of these sensors is based on a 555 circuit in an astable configuration coupled to an Arduino to estimate and quantify the nectar. Field trials validated the viability of the sensor, confirming its ability to estimate the presence of nectar by applying the Avrami model to the signal obtained. Subsequently, a simulation was carried out in Python with the aim of identifying the minimum parameters required for the construction and optimization of the system. In this phase, the configurable variables of the floral patch that have the greatest influence on overall efficiency were identified. To do this, an agent-based approach was adopted, expanding an existing simulation on foraging and waggle dancing with new agents representing flowers and autonomous supply. By analyzing efficiency metrics—such as nectar collected, replenishment times, and visits to empty flowers—the optimal configuration for the construction of the floral patch and its use in experiments under natural conditions was defined. This allows us to understand the interaction of bees with nature and helps us find better ways to care for them and ensure the well-being of ecosystems.
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Date
2025-07-10
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Keywords
Parche floral autonomo, Sensor de nectar, ABM