Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Jonghan, Ko"'
Publikováno v:
Ecological Informatics, Vol 84, Iss , Pp 102886- (2024)
This study introduces a novel crop modeling approach based on cutting-edge computational tools to advance crop production monitoring methodologies, and, thereby, tackle global food security issues. Our approach pioneers integrating deep learning and
Externí odkaz:
https://doaj.org/article/7917c9ac78474e20b9f8e7e63a35c006
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract A vegetation canopy chamber system measures gas exchanges in the field between plants and the environment. Transparent closed chambers have generally been used to measure canopy fluxes in the field, depending on solar radiation as the light
Externí odkaz:
https://doaj.org/article/6f5960286a3d497794d0d4a07938b878
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
Machine learning (ML) techniques offer a promising avenue for improving the integration of remote sensing data into mathematical crop models, thereby enhancing crop growth prediction accuracy. A critical variable for this integration is the leaf area
Externí odkaz:
https://doaj.org/article/262ca45d73044d3f84dcbfbb65d5f578
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-10 (2022)
Abstract Machine learning (ML) and deep neural network (DNN) techniques are promising tools. These can advance mathematical crop modelling methodologies that can integrate these schemes into a process-based crop model capable of reproducing or simula
Externí odkaz:
https://doaj.org/article/fe2071961cc540f98a7189aa5a8a44e1
Autor:
Hyunkyeong Min, Hyeon-Seok Lee, Chun-Kuen Lee, Woo-Jung Choi, Bo-Keun Ha, Hyeongju Lee, Seo-Ho Shin, Kyu-Nam An, Dong-Kwan Kim, Oh-Do Kwon, Jonghan Ko, Jaeil Cho, Han-Yong Kim
Publikováno v:
Agronomy, Vol 13, Iss 11, p 2692 (2023)
According to numerous chamber and free-air CO2 enrichment (FACE) studies with artificially raised CO2 concentration and/or temperature, it appears that increasing atmospheric CO2 concentrations ([CO2]) stimulates crop yield. However, there is still c
Externí odkaz:
https://doaj.org/article/1c4ffa12f6a9487e8ce6d7976a943e9c
Publikováno v:
Remote Sensing, Vol 15, Iss 19, p 4673 (2023)
Although the Landsat 30 m Enhanced Vegetation Index (EVI) products are important input variables in land surface models, recurring Landsat 5/7 EVI time series over cloud-prone, fragmented, and mosaic agricultural landscapes is still a great challenge
Externí odkaz:
https://doaj.org/article/8593f30f987d446fa8fb8d691114429a
Publikováno v:
Geo Data, Vol 3, Iss 2, Pp 20-24 (2021)
This study estimated rice yield maps for Northeast Asia by using the Communication, Ocean and Meteorological satellite (COMS), Terra satellite, and Regional Data Assimilation and Prediction System (RDAPS) of the numerical model. The rice yield is hig
Externí odkaz:
https://doaj.org/article/917d5cd29b784cb5994df12dc4382cb8
The spatial data of paddy rice classification over Northeast Asia using COMS geostationary satellite
Publikováno v:
Geo Data, Vol 3, Iss 1, Pp 18-22 (2021)
The Korea Aerospace Research Institute (KARI) estimated paddy rice classification maps over Northeast Asia using the Cheonian geostationary orbiting satellite (COMS: Communication, Ocean and Meteorological Satellite) data. In the case of classificati
Externí odkaz:
https://doaj.org/article/17968e95c25f49c78c0d7d1e989e9724
Publikováno v:
GIScience & Remote Sensing, Vol 58, Iss 1, Pp 1-27 (2021)
Acquiring accurate and timely information on the spatial distribution of paddy rice fields and the corresponding yield is an important first step in meeting the regional and global food security needs. In this study, using dense vegetation index prof
Externí odkaz:
https://doaj.org/article/1eb133c3217f47f4834973dfe40e1ab7
Publikováno v:
Remote Sensing, Vol 15, Iss 5, p 1408 (2023)
A remote sensing (RS) platform consisting of a remote-controlled aerial vehicle (RAV) can be used to monitor crop, environmental conditions, and productivity in agricultural areas. However, the current methods for the calibration of RAV-acquired imag
Externí odkaz:
https://doaj.org/article/be98939182bb4fa28d77c4f547590488