Ingeniería Mecatrónica

Permanent URI for this collectionhttp://172.16.0.136/handle/123456789/59

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Browsing Ingeniería Mecatrónica by Subject "Agricultura"

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    Diseño de invernadero para un cultivo de vegetales
    Durango-López, Roberto Carlos; Cruz-Guayacundo, Wilmer
    The main objective of this thesis is to develop the first phases of an intelligent greenhouse that allows the cultivation of representative vegetables of Colombian agriculture in order to improve the quality and quantity of indoor crops in the country. In order to carry out this project, we first identified several problems of the country's agricultural crops and through interviews we were able to identify the requirements requested by farmers in order to propose a satisfactory solution. With these factors, the design methodology was implemented, which initially contemplates the selection of the type of greenhouse to be developed by means of decision matrices, then the QFD quality house was used to define the product and the conceptual design was made to select the concept to be worked on, then the detailed design was made to analyze the selected concept, followed by 3D modeling with Autodesk Inventor software in which the safety factor analysis and the deformation that may occur were made, and finally the design was brought to reality by means of a prototype and a physical presentation.
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    Sistema de clasificación de tomates con visión artificial
    Chitiva-Saénz, Laura Camila; Anzola-Florido, Miguel Angel; Serrano-Puerto, Wilson Javier
    This research focuses on the development of a tomato classification machine using artificial vision techniques with the aim of improving the tomato classification process based on their quality. The study followed a methodology that involved several key steps. First, the specifications of the tomato classification system were detailed, following the NTC-1103-1 standard as a reference. Subsequently, a careful selection of the necessary hardware and software devices was conducted to ensure their suitability for image processing and analysis. The core of the research was the application of artificial vision techniques to carry out tomato classification. This phase focused on crucial criteria such as size, color, and homogeneity, which are essential visual attributes for determining tomato quality. The results revealed that the use of a neural network in this classification system provided better performance. The neural network demonstrated higher effectiveness in classifying high-quality tomatoes compared to traditional computer vision methods. This advancement represents a significant milestone in the automation of the agricultural product classification process and has the potential to reduce losses associated with manual classification, thereby improving the profitability of producers, and ensuring the delivery of high-quality products to consumers.