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View article: VegAnn: Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image segmentation
VegAnn: Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image segmentation Open
VegAnn - Vegetation Annotation - dataset, a collection of 3795 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions.
View article: VegAnn: Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image segmentation
VegAnn: Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image segmentation Open
VegAnn - Vegetation Annotation - dataset, a collection of 3795 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions.
View article: How Well Do EO-Based Food Security Warning Systems for Food Security Agree? Comparison of NDVI-Based Vegetation Anomaly Maps in West Africa
How Well Do EO-Based Food Security Warning Systems for Food Security Agree? Comparison of NDVI-Based Vegetation Anomaly Maps in West Africa Open
The GEOGLAM crop monitor for early warning is based on the integration of the crop conditions assessments produced by regional systems. Discrepancies between these assessments can occur and are generally attributed to the interpretation of…
View article: VegAnn: Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image segmentation
VegAnn: Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image segmentation Open
Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetati…
View article: SegVeg: Segmenting RGB images into green and senescent vegetation by combining deep and shallow methods
SegVeg: Segmenting RGB images into green and senescent vegetation by combining deep and shallow methods Open
The pixels segmentation of high resolution RGB images into background, green vegetation and senescent vegetation classes is a first step often required before estimating key traits of interest including the vegetation fraction, the green a…
View article: SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods
SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods Open
Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of interest. We have developed the SegVeg approach for semantic segmentati…
View article: Estimates of maize plant density from UAV RGB images using Faster-RCNN\n detection model: impact of the spatial resolution
Estimates of maize plant density from UAV RGB images using Faster-RCNN\n detection model: impact of the spatial resolution Open
Early-stage plant density is an essential trait that determines the fate of a\ngenotype under given environmental conditions and management practices. The use\nof RGB images taken from UAVs may replace traditional visual counting in fields…
View article: Global Wheat Head Dataset 2021: an update to improve the benchmarking wheat head localization with more diversity.
Global Wheat Head Dataset 2021: an update to improve the benchmarking wheat head localization with more diversity. Open
The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has
assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from
various acquisition platforms and 7 countries/institutions. With an associated
competitio…
View article: Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods
Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods Open
The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competitio…
View article: A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images
A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images Open
Multispectral observations from unmanned aerial vehicles (UAVs) are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status. However, t…
View article: Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods
Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods Open
Detection of wheat heads is an important task allowing to estimate pertinent traits including head population density and head characteristics such as sanitary state, size, maturity stage and the presence of awns. Several studies developed…
View article: The P2S2 segmentation dataset: annotated in-field multi-crop RGB images acquired under various conditions
The P2S2 segmentation dataset: annotated in-field multi-crop RGB images acquired under various conditions Open
International audience
View article: High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass
High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass Open
Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes. Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and ni…
View article: Wheat-Ears-Detection-Dataset
Wheat-Ears-Detection-Dataset Open
Dataset from the Ear density estimation from high resolution RGB imagery using deep learning technique paper Highlights - 236 high resolution images (6000*4000) - Wheat ears annotated with a bounding box - 30729 ears identified - Spatial r…
View article: Wheat-Ears-Detection-Dataset
Wheat-Ears-Detection-Dataset Open
Dataset from the Ear density estimation from high resolution RGB imagery using deep learning technique paper Highlights - 236 high resolution images (6000*4000) - Wheat ears annotated with a bounding box - 30729 ears identified - Spatial r…
View article: Leaf-rolling in maize crops: from leaf scoring to canopy-level measurements for phenotyping
Leaf-rolling in maize crops: from leaf scoring to canopy-level measurements for phenotyping Open
Leaf rolling in maize crops is one of the main plant reactions to water stress that can be visually scored in the field. However, leaf-scoring techniques do not meet the high-throughput requirements needed by breeders for efficient phenoty…
View article: High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates
High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates Open
The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalit…
View article: Leaf rolling in maize crops: from leaf scoring to canopy level measurements for phenotyping
Leaf rolling in maize crops: from leaf scoring to canopy level measurements for phenotyping Open
Leaf rolling in maize crops is one of the main plant reactions to water stress that may be visually scored in the field. However, the leaf scoring did not reach the high-throughput desired by breeders for efficient phenotyping. This study …
View article: The problem of radiometric calibration for UAV observations acquired under changign illumination conditions
The problem of radiometric calibration for UAV observations acquired under changign illumination conditions Open
Growing attention has been recently given to the use of UAVs for the acquisition of very high spatial resolution multispectral imagery, for agriculture applications. Crop monitoring requires the derivation of state variables such as green …