10 research outputs found
Machine Vision System for Early-stage Apple Flowers and Flower Clusters Detection for Precision Thinning and Pollination
Early-stage identification of fruit flowers that are in both opened and
unopened condition in an orchard environment is significant information to
perform crop load management operations such as flower thinning and pollination
using automated and robotic platforms. These operations are important in
tree-fruit agriculture to enhance fruit quality, manage crop load, and enhance
the overall profit. The recent development in agricultural automation suggests
that this can be done using robotics which includes machine vision technology.
In this article, we proposed a vision system that detects early-stage flowers
in an unstructured orchard environment using YOLOv5 object detection algorithm.
For the robotics implementation, the position of a cluster of the flower
blossom is important to navigate the robot and the end effector. The centroid
of individual flowers (both open and unopen) was identified and associated with
flower clusters via K-means clustering. The accuracy of the opened and unopened
flower detection is achieved up to mAP of 81.9% in commercial orchard images
Performance of ChatGPT on USMLE: Unlocking the Potential of Large Language Models for AI-Assisted Medical Education
Artificial intelligence is gaining traction in more ways than ever before.
The popularity of language models and AI-based businesses has soared since
ChatGPT was made available to the general public via OpenAI. It is becoming
increasingly common for people to use ChatGPT both professionally and
personally. Considering the widespread use of ChatGPT and the reliance people
place on it, this study determined how reliable ChatGPT can be for answering
complex medical and clinical questions. Harvard University gross anatomy along
with the United States Medical Licensing Examination (USMLE) questionnaire were
used to accomplish the objective. The paper evaluated the obtained results
using a 2-way ANOVA and posthoc analysis. Both showed systematic covariation
between format and prompt. Furthermore, the physician adjudicators
independently rated the outcome's accuracy, concordance, and insight. As a
result of the analysis, ChatGPT-generated answers were found to be more
context-oriented and represented a better model for deductive reasoning than
regular Google search results. Furthermore, ChatGPT obtained 58.8% on logical
questions and 60% on ethical questions. This means that the ChatGPT is
approaching the passing range for logical questions and has crossed the
threshold for ethical questions. The paper believes ChatGPT and other language
learning models can be invaluable tools for e-learners; however, the study
suggests that there is still room to improve their accuracy. In order to
improve ChatGPT's performance in the future, further research is needed to
better understand how it can answer different types of questions.Comment: 12 pages, 4 Figues, 4 table
Robotic Pollination of Apples in Commercial Orchards
This research presents a novel, robotic pollination system designed for
targeted pollination of apple flowers in modern fruiting wall orchards.
Developed in response to the challenges of global colony collapse disorder,
climate change, and the need for sustainable alternatives to traditional
pollinators, the system utilizes a commercial manipulator, a vision system, and
a spray nozzle for pollen application. Initial tests in April 2022 pollinated
56% of the target flower clusters with at least one fruit with a cycle time of
6.5 s. Significant improvements were made in 2023, with the system accurately
detecting 91% of available flowers and pollinating 84% of target flowers with a
reduced cycle time of 4.8 s. This system showed potential for precision
artificial pollination that can also minimize the need for labor-intensive
field operations such as flower and fruitlet thinning.Comment: 2 Page, 1 figur
Using Computer Vision to Track Facial Color Changes and Predict Heart Rate
The current technological advances have pushed the quantification of exercise intensity to new era of physical exercise sciences. Monitoring physical exercise is essential in the process of planning, applying, and controlling loads for performance optimization and health. A lot of research studies applied various statistical approaches to estimate various physiological indices, to our knowledge, no studies found to investigate the relationship of facial color changes and increased exercise intensity. The aim of this study was to develop a non-contact method based on computer vision to determine the heart rate and, ultimately, the exercise intensity. The method was based on analyzing facial color changes during exercise by using RGB, HSV, YCbCr, Lab, and YUV color models. Nine university students participated in the study (mean age = 26.88 ± 6.01 years, mean weight = 72.56 ± 14.27 kg, mean height = 172.88 ± 12.04 cm, six males and three females, and all white Caucasian). The data analyses were carried out separately for each participant (personalized model) as well as all the participants at a time (universal model). The multiple auto regressions, and a multiple polynomial regression model were designed to predict maximum heart rate percentage (maxHR%) from each color models. The results were analyzed and evaluated using Root Mean Square Error (RMSE), F-values, and R-square. The multiple polynomial regression using all participants exhibits the best accuracy with RMSE of 6.75 (R-square = 0.78). Exercise prescription and monitoring can benefit from the use of these methods, for example, to optimize the process of online monitoring, without having the need to use any other instrumentation
A Review on Computer Vision Technology for Physical Exercise Monitoring
Physical activity is movement of the body or part of the body to make muscles more active and to lose the energy from the body. Regular physical activity in the daily routine is very important to maintain good physical and mental health. It can be performed at home, a rehabilitation center, gym, etc., with a regular monitoring system. How long and which physical activity is essential for specific people is very important to know because it depends on age, sex, time, people that have specific diseases, etc. Therefore, it is essential to monitor physical activity either at a physical activity center or even at home. Physiological parameter monitoring using contact sensor technology has been practiced for a long time, however, it has a lot of limitations. In the last decades, a lot of inexpensive and accurate non-contact sensors became available on the market that can be used for vital sign monitoring. In this study, the existing research studies related to the non-contact and video-based technologies for various physiological parameters during exercise are reviewed. It covers mainly Heart Rate, Respiratory Rate, Heart Rate Variability, Blood Pressure, etc., using various technologies including PPG, Video analysis using deep learning, etc. This article covers all the technologies using non-contact methods to detect any of the physiological parameters and discusses how technology has been extended over the years. The paper presents some introductory parts of the corresponding topic and state of art review in that area
A Review on Computer Vision Technology for Physical Exercise Monitoring
Physical activity is movement of the body or part of the body to make muscles more active and to lose the energy from the body. Regular physical activity in the daily routine is very important to maintain good physical and mental health. It can be performed at home, a rehabilitation center, gym, etc., with a regular monitoring system. How long and which physical activity is essential for specific people is very important to know because it depends on age, sex, time, people that have specific diseases, etc. Therefore, it is essential to monitor physical activity either at a physical activity center or even at home. Physiological parameter monitoring using contact sensor technology has been practiced for a long time, however, it has a lot of limitations. In the last decades, a lot of inexpensive and accurate non-contact sensors became available on the market that can be used for vital sign monitoring. In this study, the existing research studies related to the non-contact and video-based technologies for various physiological parameters during exercise are reviewed. It covers mainly Heart Rate, Respiratory Rate, Heart Rate Variability, Blood Pressure, etc., using various technologies including PPG, Video analysis using deep learning, etc. This article covers all the technologies using non-contact methods to detect any of the physiological parameters and discusses how technology has been extended over the years. The paper presents some introductory parts of the corresponding topic and state of art review in that area
Tracking 3D deformable objects in real time
3D object tracking is a topic that has been widely studied for several years. Although there are already several robust solutions for tracking rigid objects, when it comes to deformable objects the problem increases in complexity. In recent years, there has been an increase in the use of Machine / Deep Learning techniques to solve problems in computer vision, including 3D object tracking. On the other hand, several low-cost devices (like Kinect) have appeared that allow obtaining RGB-D images, which, in addition to colour information, contain depth information. In this paper is proposed a 3D tracking approach for deformable objects that use Machine / Deep Learning techniques and have RGB-D images as input. Furthermore, our approach implements a tracking algorithm, increasing the object segmentation performance towards real time. Our tests were performed on a dataset acquired by ourselves and have obtained satisfactory results for the segmentation of the deformable object.This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n degrees 42778; Funding Reference: POCI-010247-FEDER-042778].(undefined
Leather defect detection using semantic segmentation: A hardware platform and software prototype
Leather is a textile material made from the animal skins created through a process of tanning of hides. It is a durable material, and the price is higher compared to other types of textiles. The leather is highly sensitive to its quality and surface defect condition as it is expensive. The manual defect inspection process is tedious, labor intensive, time consuming, and often prone to human error. The aim of this research is to replace the manual process of leather inspection using fully automatic defect detection based on cutting-as machine vision techniques. The laboratorial platform consists of some mechanical components (conveyer or camera moving system), camera, lighting system, computing device (computer), and display system. In the proposed laboratorial platform, a conveyor system is used which is a fast and efficient mechanical handling apparatus for automatically transporting leather pieces during inspection. A camera is fitted above the surface of conveyor so that it can detect leather and capture and send to the computing devices. Then, a series of image processing will be carried out to detect defect detection which consist image pre-processing, training the deep learning models, and testing. The proposed semantic segmentation deep learning model was experimented using MVTEC leather dataset. We obtain 94% of Intersection of Union (IOU) in the experiments.ERDF - European Regional Development Fund(undefined