Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
Abstract Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield po...
Enregistré dans:
Auteurs principaux: | Saeed Khaki, Hieu Pham, Lizhi Wang |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/bb39e63a6f074bcbb30adf1403b105e0 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Methodology for prediction of corn yield using remote
sensing satellite data in Central Mexico
par: Jesús Soria Ruiz, et autres
Publié: (2004) -
Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters
par: Yusuke Toda, et autres
Publié: (2021) -
Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data
par: Paulo Eduardo Teodoro, et autres
Publié: (2021) -
Yield estimation of the 2020 Beirut explosion using open access waveform and remote sensing data
par: Christoph Pilger, et autres
Publié: (2021) -
Maladaptation of U.S. corn and soybeans to a changing climate
par: Chengzheng Yu, et autres
Publié: (2021)