Zobrazeno 1 - 10
of 812
pro vyhledávání: '"Sook Yoon"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Plant diseases pose significant threats to agriculture, impacting both food safety and public health. Traditional plant disease detection systems are typically limited to recognizing disease categories included in the training dataset, rende
Externí odkaz:
https://doaj.org/article/fa7e765c0ffd4abcabce60319feb53c0
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
Although plant disease recognition has witnessed a significant improvement with deep learning in recent years, a common observation is that current deep learning methods with decent performance tend to suffer in real-world applications. We argue that
Externí odkaz:
https://doaj.org/article/871d6d46a3914ddea4648d323cfbae2b
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
Externí odkaz:
https://doaj.org/article/82a4d515f4e947e9a36694941770857d
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
Previous work on plant disease detection demonstrated that object detectors generally suffer from degraded training data, and annotations with noise may cause the training task to fail. Well-annotated datasets are therefore crucial to build a robust
Externí odkaz:
https://doaj.org/article/28e7f0b3f80b4a56bfa3751738467a29
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
Plant disease detection has made significant strides thanks to the emergence of deep learning. However, existing methods have been limited to closed-set and static learning settings, where models are trained using a specific dataset. This confinement
Externí odkaz:
https://doaj.org/article/134a0d4575b44232852ed61466bd9280
Autor:
Mingle Xu, Hyongsuk Kim, Jucheng Yang, Alvaro Fuentes, Yao Meng, Sook Yoon, Taehyun Kim, Dong Sun Park
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Conseq
Externí odkaz:
https://doaj.org/article/f3c0115a09de4ca78c6bae1ba04d747f
Autor:
Ruihan Ma, Alvaro Fuentes, Sook Yoon, Woon Yong Lee, Sang Cheol Kim, Hyongsuk Kim, Dong Sun Park
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
Plant phenotyping is a critical field in agriculture, aiming to understand crop growth under specific conditions. Recent research uses images to describe plant characteristics by detecting visual information within organs such as leaves, flowers, ste
Externí odkaz:
https://doaj.org/article/b7d767e8814540e79e26b1cb9499c874
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
Externí odkaz:
https://doaj.org/article/cfd4c724e41e4120a0c30dd68f0a1966
Publikováno v:
Animals, Vol 13, Iss 22, p 3588 (2023)
Accurate identification of individual cattle is of paramount importance in precision livestock farming, enabling the monitoring of cattle behavior, disease prevention, and enhanced animal welfare. Unlike human faces, the faces of most Hanwoo cattle,
Externí odkaz:
https://doaj.org/article/0bef82a8e0d44c09a0ac135a7f79a076
Publikováno v:
IEEE Access, Vol 10, Pp 6569-6579 (2022)
We propose an enhanced class-specific spatial normalization, a simple yet effective layer to generate a photorealistic image given a spatial-class map. Under the assumption that pixels belonging to the same class share the same distribution in the fe
Externí odkaz:
https://doaj.org/article/6b87283fdbd149e182d77672b79c75da