Deep learning-based estimation of rice yield using RGB image
Crop productivity is poorly assessed globally. Here, we provide a deep learning-based approach for estimating rice yield using RGB images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downwards over the rice canopy from a distance of 0.8 to 0.9 m, and r...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Lenguaje: | Inglés |
| Publicado: |
Research Square Platform LLC
2021
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/125823 |
Ejemplares similares: Deep learning-based estimation of rice yield using RGB image
- Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images
- Robustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations
- Challenges and opportunities for improving N use efficiency for rice production in sub-Saharan Africa
- Factors affecting variation in farm yields of irrigated lowland rice in southern-central Benin
- Application of a Bayesian approach to quantify the impact of nitrogen fertilizer on upland rice yield in sub-Saharan Africa
- Phosphorus micro-dosing as an entry point to sustainable intensification of rice systems in sub-Saharan Africa