Digital twin for monitoring threshing performance of combine harvesters DOI

Dafang Guo,

Yuefeng Du,

Linze Wang

et al.

Measurement, Journal Year: 2024, Volume and Issue: 239, P. 115411 - 115411

Published: July 29, 2024

Language: Английский

A fuzzy decision-making algorithm-based header height measurement system for combine harvester DOI
Qian Wang, Junjie Zhao, Zhijun Meng

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116918 - 116918

Published: Feb. 1, 2025

Language: Английский

Citations

1
Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting DOI Creative Commons
Qian Wang,

Wuchang Qin,

Mengnan Liu

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(10), P. 1846 - 1846

Published: Oct. 19, 2024

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and stubble color interference in field, make it challenging to identify harvest accurately quickly. Therefore, this paper proposes recognition model based on MV3_DeepLabV3+ network framework, which can quickly complete identification environments. uses lightweight MobileNetV3_Large backbone LeakyReLU activation function avoid neural death problem. Depth-separable convolution introduced into Atrous Spatial Pyramid Pooling (ASPP) reduce complexity parameters. cubic B-spline curve-fitting method extracts line. A prototype harvester was built, field tests were conducted. test results show that proposed achieves segmentation accuracy 98.04% unharvested regions environments, with IoU 95.02%. When travels at 0~1.5 m/s, normal speed operation, average processing time pixel error single image are 0.15 s 7.3 pixels, respectively. This could achieve high fast speed. provides practical autonomous operation

Language: Английский

Citations

5
Technological development and optimization of pushing and grasping functions in robot arms: A review DOI
Adhan Efendi,

Yu-Hsiang Shao,

Chih-Yung Huang

et al.

Measurement, Journal Year: 2024, Volume and Issue: unknown, P. 115729 - 115729

Published: Sept. 1, 2024

Language: Английский

Citations

3
A stereoscopic video computer vision system for weed discrimination in rice field under both natural and controlled light conditions by machine learning DOI Creative Commons
Mojtaba Dadashzadeh, Yousef Abbaspour‐Gilandeh, Tarahom Mesri Gundoshmian

et al.

Measurement, Journal Year: 2024, Volume and Issue: 237, P. 115072 - 115072

Published: June 12, 2024

A site-specific weed detection and classification system was implemented with a stereoscopic video camera to reduce the adverse effects of chemical herbicides in rice field. computer vision meta-heuristic hybrid NN-ICA classifier were used accurately discriminate between two varieties plants, under either natural light (NLC) or controlled conditions (CLC). Preprocessing, segmentation, matching procedures performed on images coming from right left channels. Most discriminant features selected average, arithmetic geometric, using NN-PSO algorithm. Accuracy results stereo NLC 85.71 % for mean (AM) 85.63 geometric (GM), test set. At same time, accuracy CLC reached 96.95 AM case 94.74 GM case, being consistently higher than those NLC.

Language: Английский

Citations

2
Digital twin for monitoring threshing performance of combine harvesters DOI

Dafang Guo,

Yuefeng Du,

Linze Wang

et al.

Measurement, Journal Year: 2024, Volume and Issue: 239, P. 115411 - 115411

Published: July 29, 2024

Language: Английский

Citations

2