Color measurement of animal source foods

Rapid and objective assessment of food color is necessary in quality control. The color evaluation of animal source foods using a computer vision system (CVS) and a traditional colorimeter is examined. With the same measurement conditions, color results deviated between these two approaches. The color returned by the CVS had a close resemblance to the perceived color of the animal source foods, whereas the colorimeter returned not typical colors. The effectiveness of the CVS is confirmed by the study results. Considering these data, it could be concluded that the colorimeter is not representative method for color analysis of animal source foods, therefore, the color read by the CVS seemed to be more similar to the real ones.

The Use of Different Image Recognition Techniques in Food Safety: A Study

Food safety refers to preparing, transporting, and storing food to avoid foodborne sickness and harm. From farm to factory and factory to fork, food items may meet various health dangers. Therefore, food safety is crucial both monetarily and morally. The implications of failing to comply with food safety requirements are varied. The requirement for accurate, quick, and nonpartisan quality assessments of these features in food products continues to rise with increased demands for dietary materials and high-quality requirements. Computer vision provides an automatic, nondestructive, and economic approach to achieving these aims. A substantial research has demonstrated its effectiveness for fruit and vegetable assessment and classification. It stresses the critical components of image processing technology and a survey of the most current advances across the food sector. This article outlines the essential parts of a computer vision system. In order to avoid foodborne disease and ensure food security, fast and effective detection of pathogenic microorganisms is crucial for public safety biomonitoring. Over the years, microorganism detection techniques have evolved.

Some features of creating a computer vision system

In the paper some features of models and algorithms of computer vision are presented. An algorithm for training the neural network of object recognition is proposed and described. The peculiarity of the proposed approach is the parallel training of networks with the subsequent selection of the most accurate. The presented results of experiments confirm the effectiveness of the proposed approach.

Automatic Asbestos Control Using Deep Learning Based Computer Vision System

The paper discusses the results of the research and development of an innovative deep learning-based computer vision system for the fully automatic asbestos content (productivity) estimation in rock chunk (stone) veins in an open pit and within the time comparable with the work of specialists (about 10 min per one open pit processing place). The discussed system is based on the applying of instance and semantic segmentation of artificial neural networks. The Mask R-CNN-based network architecture is applied to the asbestos-containing rock chunks searching images of an open pit. The U-Net-based network architecture is applied to the segmentation of asbestos veins in the images of selected rock chunks. The designed system allows an automatic search and takes images of the asbestos rocks in an open pit in the near-infrared range (NIR) and processes the obtained images. The result of the system work is the average asbestos content (productivity) estimation for each controlled open pit. It is validated to estimate asbestos content as the graduated average ratio of the vein area value to the selected rock chunk area value, both determined by the trained neural network. For both neural network training tasks the training, validation, and test datasets are collected. The designed system demonstrates an error of about 0.4% under different weather conditions in an open pit when the asbestos content is about 1.5–4%. The obtained accuracy is sufficient to use the system as a geological service tool instead of currently applied visual-based estimations.

Application of a computer vision system for recognizing tomato fruits and determining their position relative to the gripper device of the harvesting robot

The article presents a method for recognizing tomato fruits covered with foliage, de-termining their centers and boundaries using the OpenCV computer vision library and a hardware complex based on Raspberry Pi 4. The methods for solving the inverse kinematics problem for the five-link robotic manipulator designed by the authors, installed on a mobile plat-form, in order to create a robot for collecting fruits are considered. The simulation of the manipulator movement in the Scilab environment is performed.