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    Sistema de estimación de producción de cultivo de mora empleando visión artificial y machine learning
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Aldaz Saca Fabricio Javier; Ibarra Rojano Gilber Andrés; Córdova Córdova Edgar Patricio
    Blackberry cultivation is a significant sector of the national economy due to its high demand at both local and international levels. However, growers face considerable challenges in attempting to accurately predict production. Conventional methods, which rely heavily on manual procedures, are often inaccurate and prone to human error, potentially compromising planning and resulting in economic losses. The system developed in this research is composed of four fundamental stages: acquisition, processing, training, and analysis. For data collection, a DJI Mini 2 unmanned aerial vehicle (UAV) was utilized, capable of capturing real-time images of the crops. During processing, these images were analyzed using computer vision techniques, employing tools such as OpenCV and the YOLOv8m detection model. The model was trained with a specific dataset that included photographs of blackberries at different stages of maturity: green, red, and purple. To project potential losses due to factors such as pests, adverse weather, and drought, a Monte Carlo-based simulation was integrated. The results were presented in a graphical interface designed to facilitate visualization and analysis, assisting growers in optimizing their decision-making. The system exhibited 80.55% reliability in blackberry identification and counting, with data captured directly in the field. Furthermore, the simulations yielded a comprehensive evaluation of potential adverse scenarios, enabling more precise and realistic estimates.
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    Sistema cuantificador de calidad de cultivo de manzana para monitoreo de la producción utilizando algoritmos de Aprendizaje Profundo con Visión Artificial y Segmentación de Instancias
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Maestría en Electrónica y Automatización, 2022) Garcés Cadena, Andrés Alejandro; Prado Romo, Álvaro Javier
    Nowadays, agriculture is an activity of marked influence in the economy world, therefore, in order to satisfy the progressive food needs, human beings have been introducing technological tools for the optimization of agricultural practices, this management is also known as Precision Agriculture (PA) Artificial Vision is a technology that has given greater support to Precision Agriculture (PA), granting a wide range of tools with the ability to reduce difficulties faced by the farmer during his hand labor. The aim of this project is to provide farmers a tool to improve the process for apple harvest management, by using Deep Learning (DL) algorithms and a Computer Vision system. The system development includes two study analyses: apple type detection and quality quantification for its inspection and validation using a non-invasive method. For apple type detection, SSD-MobileNet model was used and for apple quality segmentation, a fully convolutional network FCN-ResNet-18 was used. For both studies, networks were retrained with customized databases generated specifically for the development of this project. Lastly, evaluation parameters of the detection and segmentation systems are presented with metrics such as confusion matrices, and overlapping of objects on the IoU, respectively.