The article presents the development of an Algorithm with Computer Vision using YOLOv8 Neural Networks for Blueberry Quality Inspection. The YOLOv8 network was employed for the detection and classification of blueberries based on their quality. A total of 840 images of blueberries were collected and labeled using the Roboflow platform. After training and evaluating the model, an accuracy ranging from 89% to 96%, and F1-Scores between 90% and 97% were achieved in classifying blueberries as good or bad across seven different production zones. The results demonstrate the effectiveness of the YOLOv8-based computer vision system for accurately detecting blueberry quality, optimizing the selection process, and reducing human intervention.

This study employed YOLOv8, an advanced neural network, to develop a real-time artificial vision system for detecting and counting people. A total of 7250 images were collected using Roboflow to train the model, enhancing its accuracy through data augmentation techniques. The training process leveraged a Tesla T4 GPU on Google Colab for accelerated processing. The system achieved an average accuracy of 94.6%, with peaks reaching 100% at specific times, albeit encountering some false positives. These findings underscore YOLOv8's effectiveness in enhancing security and crowd management, suggesting future enhancements in model confidence and image quality could further improve performance.

In this study, details the creation of a computer vision algorithm to verify the proper use of personal protective equipment (EPP). Using convolutional neural networks (CNN) together with tools such as Python 3.12 and YOLO V8, the system has been developed to identify in real time whether a person is wearing a mask and a headgear correctly. The accuracy of the system has been high thanks to its thorough training and validation, reaching a 96.57% validation rate. This approach is crucial to ensure that industries comply with Good Manufacturing Practices (GMP), thus guaranteeing consumer health and product quality. The development of the program included the installation and use of libraries such as OpenCV, Roboflow, MTCNN, Matplotlib, Imutils, Numpy and OS, which facilitated the detection of faces and EPPs, with training based on a database of more than 1500 labeled images.

This research determines how the implementation of a logistics management system enhances the productivity of Majorel SP Solutions SA, Lima 2023. The study is applied, featuring a non-correlational and cross-sectional design. The population under study encompasses all interactions handled by employees, ranging from warehouse staff to high-level administration. The diagnostic phase identified the main reasons behind returns, which include late deliveries, defective products, products nearing expiration, and incorrect orders. These issues stem from inadequate order planning and inefficient route planning. Furthermore, critical areas in logistics management requiring attention were highlighted, such as order distribution and storage. The proposed solutions include the implementation of the proposed logistics management processes, the application of the 5S methodology, and the ABC classification of products based on sales. Additionally, the inventory management model based on the Periodic Review model (Model P) and the redistribution of layout using the Systematic Layout Planning (SLP) method and Guerchet method were detailed. The conclusions reveal a significant increase in efficacy by 10.8%, efficiency by 28.6%, and productivity rose to 89.0%. The positive impact on overall efficiency is supported by a p-value of 0.032.