Accurate Prediction of 327 Rice Variety Growth Period Based on Unmanned Aerial Vehicle Multispectral Remote Sensing DOI Creative Commons
Zixuan Qiu, Hao Liu, Lu Wang

et al.

Drones, Journal Year: 2024, Volume and Issue: 8(11), P. 665 - 665

Published: Nov. 10, 2024

Most rice growth stage predictions are currently based on a few varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build models that tend have poor generalization ability, low accuracy, face various challenges. In this study, multispectral images of at stages were captured an unmanned aerial vehicle, single-plant silhouettes identified 327 by establishing deep-learning algorithm. A was established the normalized vegetation index combined with cubic polynomial regression equations simulate their changes, it first proposed different inferred analyzing difference rate. Overall, contour recognition model showed good ability varieties, most accuracies in range 0.75–0.93. The accuracy recognizing also some variation, root mean square error between 0.506 3.373 days, relative 2.555% 14.660%, Bias between1.126 2.358 0.787% 9.397%; therefore, can be used effectively improve periods rice.

Language: Английский

Dense Buddha head object detection and counting YOLOv8 network based on multi-scale attention and data augmentation fusion DOI Creative Commons
Yang Li,

Yalun Wang,

Dong Sui

et al.

Published: Feb. 22, 2025

Language: Английский

Citations

0
YOLOv8-Based Photovoltaic Module Detection Using Aerial Imagery DOI
N. Kellil,

C. Moussaoui,

A. Mellit

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 321 - 329

Published: Jan. 1, 2025

Language: Английский

Citations

0
Product Detection in Unmanned Supermarkets Based on Optimized YOLOv8 DOI
Fei Zhao, Liang Gao,

Yang He

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 233 - 240

Published: Jan. 1, 2025

Language: Английский

Citations

0
Recognition of Cordyceps Based on Machine Vision and Deep Learning DOI Creative Commons
Z. C. Xia, Aimin Sun, Huei‐Tse Hou

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(7), P. 713 - 713

Published: March 27, 2025

In a natural environment, due to the small size of caterpillar fungus, its indistinct features, similar color surrounding weeds and background, overlapping instances identifying fungus poses significant challenges. To address these issues, this paper proposes new MRAA network, which consists feature fusion pyramid network (MRFPN) backbone N-CSPDarknet53. MRFPN is used solve problem weak features. N-CSPDarknet53, Da-Conv module proposed background interference problems in shallow maps. The significantly improves accuracy, achieving an accuracy rate 0.202 APS for small-target recognition, represents 12% increase compared baseline 0.180 APS. Additionally, model (9.88 M), making it lightweight. It easy deploy embedded devices, greatly promotes development application identification.

Language: Английский

Citations

0
Identification of Hollow Edamame Using HSI Based on Deep Learning DOI
Shenghong Li,

Xiangquan Gao,

Shangsheng Qin

et al.

Food Control, Journal Year: 2025, Volume and Issue: unknown, P. 111329 - 111329

Published: April 1, 2025

Language: Английский

Citations

0
Simulation-Based Performance Assessment of ORB, YOLOv8, and Picking Strategies for Single-Arm Robot Conveyor Belt Pick-and-Place Operations DOI
Du Q. Huynh,

Huan Thien Tran

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

Abstract Pick-and-place robots play a crucial role in industrial automation, helping to lower labor costs, minimize errors, and improve production efficiency. Many image processing methods have been proposed facilitate the pick-and-place operation. However, performance of these is sensitive lighting conditions, presence occlusions, variations object appearance. Although many challenges can be overcome through use deep learning methods, direct comparison coupled with an analysis different picking strategies, lacking. The present study addresses this gap by conducting simulation-based evaluation accuracy time ORB algorithm YOLOv8 model for recognition. effects two strategies (FIFO Euclidean Distance) on system throughput are also explored. simulation results show that achieves higher (98%) significantly faster (138 ms) than (97.33% 715.24 ms time). Additionally, FIFO strategy improves productivity 13% compared Distance strategy. Overall, findings provide valuable insights into optimizing robotic operations automation settings.

Language: Английский

Citations

0
Enhanced YOLOv8 for Efficient Parcel Identification in Disordered Logistics Environments DOI Creative Commons
Han Yu,

Zhang Fengshou,

Zhuang Gaoshuai

et al.

International Journal of Computational Intelligence Systems, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 7, 2025

Language: Английский

Citations

0
Yolo-Sbc: Swin Transformer Combined with Modified Yolo Framework for Pcb Defect Detection DOI

S. Han,

Di Zhou, Xiao Zhuang

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
DSOD-YOLO: A lightweight dual feature extraction method for small target detection DOI

Yuan Nie,

Huicheng Lai, Guxue Gao

et al.

Digital Signal Processing, Journal Year: 2025, Volume and Issue: unknown, P. 105268 - 105268

Published: April 1, 2025

Language: Английский

Citations

0
An Automated Clubbed Fingers Detection System Based on YOLOv8 and U-Net: A Tool for Early Prediction of Lung and Cardiovascular Diseases DOI Creative Commons
Wen-Shin Hsu,

G.-Y. Liu,

Sujuan Chen

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(19), P. 2234 - 2234

Published: Oct. 7, 2024

Background/Objectives: Lung and cardiovascular diseases are leading causes of mortality worldwide, yet early detection remains challenging due to the subtle symptoms. Digital clubbing, characterized by bulbous enlargement fingertips, serves as an indicator these diseases. This study aims develop automated system for detecting digital clubbing using deep-learning models real-time monitoring intervention. Methods: The proposed utilizes YOLOv8 model object U-Net image segmentation, integrated with ESP32-CAM development board capture analyze finger images. severity is determined a custom algorithm based on Lovibond angle theory, categorizing condition into normal, mild, moderate, severe. was evaluated 1768 images achieved cloud-based processing capabilities. Results: demonstrated high accuracy (98.34%) in precision (98.22%), sensitivity (99.48%), specificity (98.22%). Cloud-based slightly lower but robust results, 96.38%. average time 0.15 s per image, showcasing its potential. Conclusions: provides scalable cost-effective solution enabling timely intervention lung Its capabilities make it suitable both clinical home-based health monitoring.

Language: Английский

Citations

1