In recent decades, it has been shown that epidemiological surveillance is one of the most valuable tool that public health has, since it allows us to have an overview of the population general health, thus allowing to anticipate outbreaks of epidemics by helping in timely interventions. Currently there is an increase in cases of dengue disease in several regions of Peru. Therefore, to control this outbreak and to help population centers and human settlements that are far from the city this work puts forward a drone system with an object recognition algorithm. Drones are very efficient in terms of surveillance, allowing easy access to places that are difficult for humans. In this way, drones can carry out the field work that is required in epidemiological surveillance, carrying out photography or video work in real time, and thus identifying infectious foci of diverse diseases. In this work, an object detection algorithm that uses convolutional neural networks and a stable detection model is designed, this allows the detection of water reservoirs that are possible infectious sources of dengue. In addition the efficiency of the algorithm is evaluated through the statistical curves of precision and sensitivity that result of the training of the neural network. To validate the efficiency obtained, the model was applied to test images related to dengue, achieving an efficiency of 99.2%.
La presente investigación tiene como propósito conocer en qué medida un sistema de automático controlado por algoritmo, permite la clasificación óptima de los frutos de café según el grado de madurez identificándolos por su color. Para lo cual se desarrolló una red neuronal multicapa empleando MATLAB el cual se implementó en un microcontrolador STM32F103C8, empleando como datos de entrada las características de modo de color RGB de 300 muestras de frutos de café en distintos estados de maduración, entregadas por un sensor de color TCS3200, que permitió contar con una base de datos de distintos niveles de madurez empleados para entrenar la red neuronal tipo multicapa con 3 entradas; 3 capas ocultas con 6 neuronas en la primera capa y 3 en las otras dos, así como una neurona en la capa de salida. Los datos fueron organizados de acuerdo al estado de madurez de los frutos, en “Madurez óptima” o “Madurez No Óptima”. Se probó el sistema con 60 frutos de café, consiguiendo como resultado una eficiencia del 96,67% y un porcentaje de error de 3,33%; confirmando así, que el sistema de clasificación mediante el control del algoritmo y red neuronal multicapa diseñado, identifica y clasifica en base a la madurez de los frutos de café manera óptima.
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