MobileNet-YOLO v5s: An Improved Lightweight Method for Real-Time Detection of Sugarcane Stem Nodes in Complex Natural Environments

Autor: Kang Yu, Guoxin Tang, Wen Chen, Shanshan Hu, Yanzhou Li, Haibo Gong

Publikováno v: IEEE Access, Vol 11, Pp 104070-104083 (2023)

To improve the precision of intelligent sugarcane harvesting, and to meet the requirements of high precision and low complexity for use with embedded devices, a lightweight model called MobileNet v2-YOLO v5s for the real-time detection of sugarcane s

Externí odkaz: https://doaj.org/article/0343ff6dd7ed4959a473a534de9e5bfb

Sugarcane stem node detection and localization for cutting using deep learning

Autor: Weiwei Wang, Cheng Li, Kui Wang, Lingling Tang, Pedro Final Ndiluau, Yuhe Cao

Publikováno v: Frontiers in Plant Science, Vol 13 (2022)

IntroductionIn order to promote sugarcane pre-cut seed good seed and good method planting technology, we combine the development of sugarcane pre-cut seed intelligent 0p99oposeed cutting machine to realize the accurate and fast identification and cut

Externí odkaz: https://doaj.org/article/aac8f1bcd70544ceb479b5897243f2bc

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Sugarcane stem nodes based on the maximum value points of the vertical projection function

Autor: Jiping Chen, Liu Xu, Huang Renzhi, Mo Rongxian, Xu Guanwen, Hu Qiang, Jianhua Wu

Publikováno v: Ciência Rural v.50 n.12 2020
Ciência Rural
Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
Ciência Rural, Volume: 50, Issue: 12, Article number: e20190797, Published: 23 OCT 2020
Ciência Rural, Vol 50, Iss 12 (2020)

In order to solve the problem that the stem nodes are difficult to identify in the process of sugarcane seed automatic cutting, a method of identifying the stem nodes of sugarcane based on the extreme points of vertical projection function is propose

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54bdf55cf4bc672a2e587d035db3eb92
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020001200201