Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Mingle Xu"'
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
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
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
Data-centric annotation analysis for plant disease detection: Strategy, consistency, and performance
Autor:
Jiuqing Dong, Jaehwan Lee, Alvaro Fuentes, Mingle Xu, Sook Yoon, Mun Haeng Lee, Dong Sun Park
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improved the performance by ameliorating networks and optimizing the loss function. However, because of the va
Externí odkaz:
https://doaj.org/article/919bff7eea2b426182efbec70db78534
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
Deep learning has witnessed a significant improvement in recent years to recognize plant diseases by observing their corresponding images. To have a decent performance, current deep learning models tend to require a large-scale dataset. However, coll
Externí odkaz:
https://doaj.org/article/1c391992d42649269aef87558ee4f15e
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
Predicting plant growth is a fundamental challenge that can be employed to analyze plants and further make decisions to have healthy plants with high yields. Deep learning has recently been showing its potential to address this challenge in recent ye
Externí odkaz:
https://doaj.org/article/428ce0b0e08041d585a60964921017b9
Publikováno v:
IEEE Access, Vol 9, Pp 111802-111813 (2021)
Instance-level image translation aims to only translate instance of interest and can be operated more finely and flexibly than object-level and holistic-level image translation. However, current algorithms are not suitable to do it since they employ
Externí odkaz:
https://doaj.org/article/42ffa9cd61854250904465ad35e76a0a
Publikováno v:
Frontiers in Plant Science, Vol 12 (2022)
Deep learning shows its advantages and potentials in plant disease recognition and has witnessed a profound development in recent years. To obtain a competing performance with a deep learning algorithm, enough amount of annotated data is requested bu
Externí odkaz:
https://doaj.org/article/21f815e6dd324593b941a9180b62cced
Publikováno v:
IEEE Access, Vol 5, Pp 18506-18515 (2017)
To balance the convergence speed and the solution's diversity in the large-scale travel salesman problem (TSP), this paper proposes a new heuristic communication heterogeneous dual population ant colony optimization (HHACO). First, the main character
Externí odkaz:
https://doaj.org/article/6ca9c010fe7244be9bb086875a5b645a
