Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Lufeng Mo"'
Publikováno v:
Remote Sensing, Vol 16, Iss 15, p 2814 (2024)
With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, i
Externí odkaz:
https://doaj.org/article/eac1b9221eb14ab5ac8254b715ac0c44
Publikováno v:
Frontiers in Environmental Science, Vol 12 (2024)
Land cover classification is of great value and can be widely used in many fields. Earlier land cover classification methods used traditional image segmentation techniques, which cannot fully and comprehensively extract the ground information in remo
Externí odkaz:
https://doaj.org/article/629e6cacf0a748ed8f2300527df6b980
Publikováno v:
Agronomy, Vol 14, Iss 6, p 1197 (2024)
Pests have caused significant losses to agriculture, greatly increasing the detection of pests in the planting process and the cost of pest management in the early stages. At this time, advances in computer vision and deep learning for the detection
Externí odkaz:
https://doaj.org/article/3e1910abf56c4b1989eaa75e065eb0ff
Autor:
Xiaomei Yi, Hanyu Chen, Peng Wu, Guoying Wang, Lufeng Mo, Bowei Wu, Yutong Yi, Xinyun Fu, Pengxiang Qian
Publikováno v:
Agronomy, Vol 14, Iss 6, p 1285 (2024)
Fast and accurate counting and positioning of flowers is the foundation of automated flower cultivation production. However, it remains a challenge to complete the counting and positioning of high-density flowers against a complex background. Therefo
Externí odkaz:
https://doaj.org/article/0c86ac24331244e0a872785890498077
Publikováno v:
Symmetry, Vol 16, Iss 6, p 723 (2024)
Target detection algorithms can greatly improve the efficiency of tomato leaf disease detection and play an important technical role in intelligent tomato cultivation. However, there are some challenges in the detection process, such as the diversity
Externí odkaz:
https://doaj.org/article/80a8d0f6aa89406ebed1748a5126824e
Autor:
Xiaomei Yi, Yue Zhou, Peng Wu, Guoying Wang, Lufeng Mo, Musenge Chola, Xinyun Fu, Pengxiang Qian
Publikováno v:
Agronomy, Vol 14, Iss 5, p 925 (2024)
Currently, the classification of grapevine black rot disease relies on assessing the percentage of affected spots in the total area, with a primary focus on accurately segmenting these spots in images. Particularly challenging are cases in which lesi
Externí odkaz:
https://doaj.org/article/c612b24c3b0d45fc8cc57e4605cf7e18
Autor:
Peng Wu, Junjie Fu, Xiaomei Yi, Guoying Wang, Lufeng Mo, Brian Tapiwanashe Maponde, Hao Liang, Chunling Tao, WenYing Ge, TengTeng Jiang, Zhen Ren
Publikováno v:
Frontiers in Remote Sensing, Vol 4 (2023)
Introduction: Monitoring surface water through the extraction of water bodies from high-resolution remote sensing images is of significant importance. With the advancements in deep learning, deep neural networks have been increasingly applied to high
Externí odkaz:
https://doaj.org/article/3debb7c66bb148e09b71015815eebf82
Autor:
Xiaomei Yi, Jiaoping Wang, Peng Wu, Guoying Wang, Lufeng Mo, Xiongwei Lou, Hao Liang, Huahong Huang, Erpei Lin, Brian Tapiwanashe Maponde, Chaihui Lv
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
Plant phenotypic traits play an important role in understanding plant growth dynamics and complex genetic traits. In phenotyping, the segmentation of plant organs, such as leaves and stems, helps in automatically monitoring growth and improving scree
Externí odkaz:
https://doaj.org/article/99af349110fc469b8688107230faf601
Publikováno v:
Animals, Vol 14, Iss 3, p 499 (2024)
Blurry scenarios, such as light reflections and water ripples, often affect the clarity and signal-to-noise ratio of fish images, posing significant challenges for traditional deep learning models in accurately recognizing fish species. Firstly, deep
Externí odkaz:
https://doaj.org/article/c7a8f8ab9cb047cdbf03d08adf99aecb
Publikováno v:
Drones, Vol 7, Iss 5, p 326 (2023)
Anomaly detection has an important impact on the development of unmanned aerial vehicles, and effective anomaly detection is fundamental to their utilization. Traditional anomaly detection discriminates anomalies for single-dimensional factors of sen
Externí odkaz:
https://doaj.org/article/52a7a7a333ec4ccb907baf62d154fbe6