Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Ming-Der Yang"'
Autor:
Dong-Hong Wu, Chung-Tse Chen, Ming-Der Yang, Yi-Chien Wu, Chia-Yu Lin, Ming-Hsin Lai, Chin-Ying Yang
Publikováno v:
Botanical Studies, Vol 63, Iss 1, Pp 1-12 (2022)
Abstract Background Rice is a key global food crop. Rice lodging causes a reduction in plant height and crop yield, and rice is prone to lodging in the late growth stage because of panicle initiation. We used two water irrigation modes and four ferti
Externí odkaz:
https://doaj.org/article/7375c947d7ac4b1bb1a055e6b12bcca7
Autor:
Ming-Der Yang Hui-Ping Tsai
Publikováno v:
Terrestrial, Atmospheric and Oceanic Sciences, Vol 30, Iss 4, Pp 493-508 (2019)
The present study utilized satellite imagery and the Normalized Difference Vegetation Index (NDVI) to investigate the post-earthquake spatio-temporal changes of landslide for Huisun Experimental Forest Station (HEFS). Total 26 SPOT satellite images t
Externí odkaz:
https://doaj.org/article/7f75577d31ec49cc9f4fc1a2d4a9251b
Publikováno v:
Remote Sensing, Vol 14, Iss 12, p 2837 (2022)
To meet demand for agriculture products, researchers have recently focused on precision agriculture to increase crop production with less input. Crop detection based on computer vision with unmanned aerial vehicle (UAV)-acquired images plays a vital
Externí odkaz:
https://doaj.org/article/3cacda62277c47bea1e7a78908107037
Autor:
Kai-Yun Li, Raul Sampaio de Lima, Niall G. Burnside, Ele Vahtmäe, Tiit Kutser, Karli Sepp, Victor Henrique Cabral Pinheiro, Ming-Der Yang, Ants Vain, Kalev Sepp
Publikováno v:
Remote Sensing, Vol 14, Iss 5, p 1114 (2022)
The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resol
Externí odkaz:
https://doaj.org/article/f9415829012240e3b8881d7eef3be213
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 11, Iss 8, Pp 2862-2868 (2018)
Oyster racks identification relies on manual in situ assessment and often leads to a compensation dispute in aquacultural damage assessment. This study proposes an efficient classification method to identify oyster racks using unmanned aerial vehicle
Externí odkaz:
https://doaj.org/article/b8dac93ce9bf4dc0baab000da888b2e1
Autor:
Guan-Sin Li, Dong-Hong Wu, Yuan-Chih Su, Bo-Jein Kuo, Ming-Der Yang, Ming-Hsin Lai, Hsiu-Ying Lu, Chin-Ying Yang
Publikováno v:
Agronomy, Vol 11, Iss 8, p 1626 (2021)
Rice is a staple food crop in Asia. The rice farming industry has been influenced by global urbanization, rapid industrialization, and climate change. A combination of precise agricultural and smart water management systems to investigate the nutriti
Externí odkaz:
https://doaj.org/article/4263fc51f30043c7b83a52d1204938ea
Autor:
Kai-Yun Li, Niall G. Burnside, Raul Sampaio de Lima, Miguel Villoslada Peciña, Karli Sepp, Victor Henrique Cabral Pinheiro, Bruno Rucy Carneiro Alves de Lima, Ming-Der Yang, Ants Vain, Kalev Sepp
Publikováno v:
Remote Sensing, Vol 13, Iss 16, p 3190 (2021)
The recent trend of automated machine learning (AutoML) has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployab
Externí odkaz:
https://doaj.org/article/7611afaf16d84f21b0a0bc97c4f43065
Autor:
Ming-Der Yang, Yu-Chun Hsu, Wei-Cheng Tseng, Chian-Yu Lu, Chin-Ying Yang, Ming-Hsin Lai, Dong-Hong Wu
Publikováno v:
Sensors, Vol 21, Iss 17, p 5875 (2021)
Grain moisture content (GMC) is a key indicator of the appropriate harvest period of rice. Conventional testing is time-consuming and laborious, thus not to be implemented over vast areas and to enable the estimation of future changes for revealing o
Externí odkaz:
https://doaj.org/article/4f4dd93fe60c450792d92a49e86afcfb
Autor:
Kai-Yun Li, Niall G. Burnside, Raul Sampaio de Lima, Miguel Villoslada Peciña, Karli Sepp, Ming-Der Yang, Janar Raet, Ants Vain, Are Selge, Kalev Sepp
Publikováno v:
Remote Sensing, Vol 13, Iss 10, p 1994 (2021)
A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture.
Externí odkaz:
https://doaj.org/article/a9af1140cac547bea6b9562e4e4d430a
Publikováno v:
Remote Sensing, Vol 13, Iss 7, p 1358 (2021)
Recently, unmanned aerial vehicles (UAVs) have been broadly applied to the remote sensing field. For a great number of UAV images, deep learning has been reinvigorated and performed many results in agricultural applications. The popular image dataset
Externí odkaz:
https://doaj.org/article/cde1d9c743334a268661bac8d98bd2bc