Similar Items: An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery
- Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure
- Pixels to pasture: Using machine learning and multispectral remote sensing to predict biomass and nutrient quality in tropical grasslands
- Editorial: Deep learning approaches applied to spectral images for plant phenotyping
- Forages ROIs: Automated forage grass detection in aerial imagery
- Aerial Monitoring of Rice Crop Variables using an UAV Robotic System
- Dataset: Forage grasses in crop fields from ultra-high spatial resolution UAV-based imagery
Author: CGIAR Research Program on Rice
- Annual report 2015: CGIAR Research Program on Rice
- Maps of rice area in Senegal River Valley based on MODIS time-series and the PhenoRice algorithm
- Three main biological/genomic resources for additional contribution to harnessing genetiv diversity
- Impact of the COVID-19 pandemic on food security and income in developing countries of stakeholders: a case study of rice value chain actors
- Impact of Nutrition Training on Long-Term Adoption of High-Zinc Rice: A Randomized Control Trial Study among Female Farmers in Bangladesh
- Genomic Selection model to predict grain Zn concentrations