Cargando…
Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting
Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time...
Autores principales: | Khaki, Saeed, Pham, Hieu, Han, Ye, Kuhl, Andy, Kent, Wade, Wang, Lizhi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249160/ https://www.ncbi.nlm.nih.gov/pubmed/32397598 http://dx.doi.org/10.3390/s20092721 |
Ejemplares similares
-
High-throughput image-based plant stand count estimation using convolutional neural networks
por: Khaki, Saeed, et al.
Publicado: (2022) -
Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
por: Khaki, Saeed, et al.
Publicado: (2021) -
A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field
por: Wang, Le, et al.
Publicado: (2021) -
Crop Yield Prediction Using Deep Neural Networks
por: Khaki, Saeed, et al.
Publicado: (2019) -
Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels
por: Hobbs, Jennifer, et al.
Publicado: (2021)