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 resolution (GSD) of 0.13mm/pixel - Two images for each microplots - 20 contrasted genotype with 6 replicated growth in two environment</p
Total above-ground biomass at harvest and ear density are two important traits that characterize whe...
International audienceBackgroundCharacterizing plant genetic resources and their response to the env...
Additional file 3. Table S1. Values for the whole set of four trials as well as within each trial, f...
Dataset from the Ear density estimation from high resolution RGB imagery using deep learning techniq...
peer reviewedRecent deep learning methods have allowed important steps forward in the automatic dete...
Initial results of field phenotyping experiments regarding ear segmentation in wheat-based on RGB an...
Overview This dataset contains 701 wheat RGB images in which all the ears have been manually labell...
The number of farmers who use smart phones is increasing rapidly and furthermore RGB and thermal cam...
Background The number of ears per unit ground area (ear density) is one of the main agronomic yield ...
Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal cro...
The number of wheat ears is an essential indicator for wheat production and yield estimation, but ac...
The detection of wheat heads in plant images is an important task for estimating pertinent wheat tra...
Presentation exploring the added value of 3D information in winter wheat ear detection compared to R...
The detection and counting of wheat ears are very important for crop field management, yield estimat...
In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the mai...
Total above-ground biomass at harvest and ear density are two important traits that characterize whe...
International audienceBackgroundCharacterizing plant genetic resources and their response to the env...
Additional file 3. Table S1. Values for the whole set of four trials as well as within each trial, f...
Dataset from the Ear density estimation from high resolution RGB imagery using deep learning techniq...
peer reviewedRecent deep learning methods have allowed important steps forward in the automatic dete...
Initial results of field phenotyping experiments regarding ear segmentation in wheat-based on RGB an...
Overview This dataset contains 701 wheat RGB images in which all the ears have been manually labell...
The number of farmers who use smart phones is increasing rapidly and furthermore RGB and thermal cam...
Background The number of ears per unit ground area (ear density) is one of the main agronomic yield ...
Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal cro...
The number of wheat ears is an essential indicator for wheat production and yield estimation, but ac...
The detection of wheat heads in plant images is an important task for estimating pertinent wheat tra...
Presentation exploring the added value of 3D information in winter wheat ear detection compared to R...
The detection and counting of wheat ears are very important for crop field management, yield estimat...
In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the mai...
Total above-ground biomass at harvest and ear density are two important traits that characterize whe...
International audienceBackgroundCharacterizing plant genetic resources and their response to the env...
Additional file 3. Table S1. Values for the whole set of four trials as well as within each trial, f...
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