Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Lizhang Xu"'
Publikováno v:
Agronomy, Vol 14, Iss 8, p 1874 (2024)
The combine harvester equipped with attitude-adjustment functionality significantly enhances its adaptability to complex terrain but often struggles to maintain the reliability of its mechanisms. Therefore, investigating the dynamic load characterist
Externí odkaz:
https://doaj.org/article/8ca6e8ce525a4fdfaa771010bd15f53d
Publikováno v:
Agriculture, Vol 14, Iss 8, p 1251 (2024)
In complex field environments, wheat grows densely with overlapping organs and different plant weights. It is difficult to accurately predict feed quantity for wheat combine harvester using the existing YOLOv5s and uniform weight of a single wheat pl
Externí odkaz:
https://doaj.org/article/52fe10dbe4b542f28b4696cfdb8afa37
Autor:
Jiahui Pan, Lizhang Xu, En Lu, Buwang Dai, Tiaotiao Chen, Weiming Sun, Zhihong Cui, Jinpeng Hu
Publikováno v:
Sensors, Vol 24, Iss 9, p 2715 (2024)
In order to enhance crop harvesting efficiency, an automatic-driving tracked grain vehicle system was designed. Based on the harvester chassis, we designed the mechanical structure of a tracked grain vehicle with a loading capacity of 4.5 m3 and a gr
Externí odkaz:
https://doaj.org/article/fd6edf07a17e49b6a18f4f75890313d1
Autor:
Shuaifeng Xing, Yang Yu, Guangqiao Cao, Jinpeng Hu, Linjun Zhu, Junyu Liu, Qinhao Wu, Qibin Li, Lizhang Xu
Publikováno v:
Agriculture, Vol 14, Iss 4, p 534 (2024)
To address the issue of reduced yield in the second season caused by damaged stubbles resulting from being compressed during the harvesting process of the first season’s ratoon rice, a device for rectifying the compressed stubbles was designed. Uti
Externí odkaz:
https://doaj.org/article/184f79da5a6e4eabad0f7fb0e8114344
Publikováno v:
IEEE Access, Vol 11, Pp 49273-49288 (2023)
It is difficult to extract small and dense objects with random state, such as grain and impurity, in image of vehicle-mounted dynamic rice grain flow on combine harvester. Therefore, this paper improves Deeplabv3+ by constructing MobileNetv2 in codin
Externí odkaz:
https://doaj.org/article/26403b21275b49a6ba649702de0e6f11
Autor:
Gongpeng Sun, Xiaoling Wang, Lizhang Xu, Chang Li, Wenyu Wang, Zuohuizi Yi, Huijuan Luo, Yu Su, Jian Zheng, Zhiqing Li, Zhen Chen, Hongmei Zheng, Changzheng Chen
Publikováno v:
Ophthalmology and Therapy, Vol 12, Iss 2, Pp 895-907 (2022)
Abstract Introduction To design and evaluate a deep learning model based on ultra-widefield images (UWFIs) that can detect several common fundus diseases. Methods Based on 4574 UWFIs, a deep learning model was trained and validated that can identify
Externí odkaz:
https://doaj.org/article/85b75414870746fe9b947e2e05a29b2b
Publikováno v:
Agriculture, Vol 13, Iss 12, p 2231 (2023)
Aimed at addressing the problems of the existing straw choppers on combine harvesters, such as a large cutting resistance and poor cutting effect, combined with bionic engineering technology and biological characteristics, a bionic model was used to
Externí odkaz:
https://doaj.org/article/24104818b1f44418bcb7db51c5245b2c
Publikováno v:
Agriculture, Vol 13, Iss 12, p 2232 (2023)
Cleaning is one of the most important steps in the harvesting process, and the prolonged and high-load operation of the vibrating sieve can decrease its reliability. To uncover the structural flaws of the cleaning sieve in the crawler combine harvest
Externí odkaz:
https://doaj.org/article/ef3161c7ca2f48e3bb932ed3f4584810
Autor:
Zhihong Cui, Lizhang Xu, Yang Yu, Xiaoyu Chai, Qian Zhang, Peng Liu, Jinpeng Hu, Yang Li, Haiwen Chen
Publikováno v:
Sensors, Vol 23, Iss 23, p 9497 (2023)
Image feature detection serves as the cornerstone for numerous vision applications, and it has found extensive use in agricultural harvesting. Nevertheless, determining the optimal feature extraction technique for a specific situation proves challeng
Externí odkaz:
https://doaj.org/article/61b5ebc71c32461abcb4631c296c032e
Publikováno v:
Agronomy, Vol 13, Iss 9, p 2227 (2023)
For the inconsistent lodging of wheat with dense growth and overlapped organs, it is difficult to detect lodging direction accurately and quickly using vehicle vision for harvesters. Therefore, in this paper, the k-means algorithm is improved by desi
Externí odkaz:
https://doaj.org/article/6a614ba55c384f16938593ca58bfb753