Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Shuolin Kong"'
Publikováno v:
IEEE Access, Vol 10, Pp 44315-44325 (2022)
To solve the problem of the low efficiency of traditional lettuce freshness classification methods and sample damage, we proposed an automatic lettuce freshness classification method based on improved deep residuals convolutional neural network (Im-R
Externí odkaz:
https://doaj.org/article/28de633897984259bdd5d34225bb4086
Publikováno v:
Agronomy, Vol 13, Iss 6, p 1503 (2023)
Crop seedlings are similar in appearance to weeds, making crop detection extremely difficult. To solve the problem of detecting crop seedlings in complex field environments, a seedling dataset with four crops was constructed in this study. The single
Externí odkaz:
https://doaj.org/article/a9a5934cb15140d5a40cf2a63e8d9f9c
Autor:
Lili Fu, Shijun Li, Shuolin Kong, Ruiwen Ni, Haohong Pang, Yu Sun, Tianli Hu, Ye Mu, Ying Guo, He Gong
Publikováno v:
PLoS ONE, Vol 17, Iss 10 (2022)
Individual cow identification is a prerequisite for intelligent dairy farming management, and is important for achieving accurate and informative dairy farming. Computer vision-based approaches are widely considered because of their non-contact and p
Externí odkaz:
https://doaj.org/article/85079694483d4e92bb2d867a26c86da7
Publikováno v:
Journal of Real-Time Image Processing. 19:985-995
Publikováno v:
Sustainability; Volume 14; Issue 14; Pages: 8915
Crop disease has been a severe issue for agriculture, causing economic loss for growers. Thus, disease identification urgently needs to be addressed, especially for precision agriculture. As of today, deep learning has been widely used for crop disea
Publikováno v:
Intelligent Automation & Soft Computing. 27:669-682