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Akademický článek

Semantic Segmentation of Corn Leaf Blotch Disease Images Based on U-Net Integrated with RFB Structure and Dual Attention Mechanism

Autor: Ye Mu, Ke Li, Yu Sun, Yu Bao

Publikováno v: Agronomy, Vol 14, Iss 11, p 2652 (2024)

Northern corn leaf blight (NCLB) is caused by a fungus and can be susceptible to the disease throughout the growing period of corn, posing a significant impact on corn yield. Aiming at the problems of under-segmentation, over-segmentation, and low se

Externí odkaz: https://doaj.org/article/82d6f38b80b7450da3a7001debfad0ee

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Akademický článek

The South America Low-Level Jet: form, variability and large-scale forcings

Autor: Charles Jones, Ye Mu, Leila M. V. Carvalho, Qinghua Ding

Publikováno v: npj Climate and Atmospheric Science, Vol 6, Iss 1, Pp 1-11 (2023)

Abstract Northerly low-level jets (LLJ) along the eastern Andes are important conduits of moisture transport and play central roles in modulating precipitation in South America. This study further investigates the variability of the LLJ during extend

Externí odkaz: https://doaj.org/article/3380298b6a0a427c8d3af1e1e4c3c510

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Akademický článek

Research on the Behavior Recognition of Beef Cattle Based on the Improved Lightweight CBR-YOLO Model Based on YOLOv8 in Multi-Scene Weather

Autor: Ye Mu, Jinghuan Hu, Heyang Wang, Shijun Li, Hang Zhu, Lan Luo, Jinfan Wei, Lingyun Ni, Hongli Chao, Tianli Hu, Yu Sun, He Gong, Ying Guo

Publikováno v: Animals, Vol 14, Iss 19, p 2800 (2024)

In modern animal husbandry, intelligent digital farming has become the key to improve production efficiency. This paper introduces a model based on improved YOLOv8, Cattle Behavior Recognition-YOLO (CBR-YOLO), which aims to accurately identify the be

Externí odkaz: https://doaj.org/article/3ea248c7127a4b79b6dcb243cb992508

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Akademický článek

GFI-YOLOv8: Sika Deer Posture Recognition Target Detection Method Based on YOLOv8

Autor: He Gong, Jingyi Liu, Zhipeng Li, Hang Zhu, Lan Luo, Haoxu Li, Tianli Hu, Ying Guo, Ye Mu

Publikováno v: Animals, Vol 14, Iss 18, p 2640 (2024)

As the sika deer breeding industry flourishes on a large scale, accurately assessing the health of these animals is of paramount importance. Implementing posture recognition through target detection serves as a vital method for monitoring the well-be

Externí odkaz: https://doaj.org/article/0d1914950d6d41d7bcd863d5773fe0c7

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Akademický článek

Improving the Accuracy of Agricultural Pest Identification: Application of AEC-YOLOv8n to Large-Scale Pest Datasets

Autor: Jinfan Wei, He Gong, Shijun Li, Minghui You, Hang Zhu, Lingyun Ni, Lan Luo, Mengchao Chen, Hongli Chao, Jinghuan Hu, Caocan Zhu, Heyang Wang, Jingyi Liu, Jiaxin Nian, Wenye Fan, Ye Mu, Yu Sun

Publikováno v: Agronomy, Vol 14, Iss 8, p 1640 (2024)

Crop diseases and pests are important factors restricting agricultural production. Traditional pest detection methods are mainly targeted at a single pest species, which is difficult to meet the needs of multi-target identification and rapid response

Externí odkaz: https://doaj.org/article/d2df5b243bf8456a8ba95847c274053b

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