Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based on high spatial resolution images provide a potential solution to increase the throughput as well as the accuracy of panicle identification. The quality and volume of the dataset are crucial to training an accurate and robust deep learning model. Panicle segmentation tasks require particularly costly annotations. Here we open a paddy rice panicle dataset, acquired by DJI Mavic Pro in 2018, to public use for rice panicle phenotyping.This version is outdated. Please see 10.5281/zenodo.4444741, the latest one that has been fully open
Additional file 5: Figure S1. Different regions in the field plot image have different illumination ...
Additional file 4: Table S2. The evaluation criterion for 48 testing rice samples using four differe...
Accurate and timely detection of phenology at plot scale in rice breeding trails is crucial for unde...
Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based ...
Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based ...
Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based ...
Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based ...
Accurate panicle identification is a key step in rice-field phenotyping. Deep learning methods based...
In this study, a lightweight phenotyping system that combined the advantages of both deep learning-b...
Accurate and rapid identification of the effective number of panicles per unit area is crucial for t...
Abstract Background Rice panicle phenotyping is important in rice breeding, and rice panicle segment...
Rice blast is regarded as one of the major diseases of rice. Screening rice genotypes with high resi...
Rice yield is closely related to the number and proportional area of rice panicles. Currently, rice ...
In this paper, UAV (unmanned aerial vehicle, DJI Phantom4RTK) and YOLOv4 (You Only Look Once) target...
Additional file 1: Video S1. The instruction of the rice panicle segmentation software.mp4
Additional file 5: Figure S1. Different regions in the field plot image have different illumination ...
Additional file 4: Table S2. The evaluation criterion for 48 testing rice samples using four differe...
Accurate and timely detection of phenology at plot scale in rice breeding trails is crucial for unde...
Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based ...
Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based ...
Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based ...
Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based ...
Accurate panicle identification is a key step in rice-field phenotyping. Deep learning methods based...
In this study, a lightweight phenotyping system that combined the advantages of both deep learning-b...
Accurate and rapid identification of the effective number of panicles per unit area is crucial for t...
Abstract Background Rice panicle phenotyping is important in rice breeding, and rice panicle segment...
Rice blast is regarded as one of the major diseases of rice. Screening rice genotypes with high resi...
Rice yield is closely related to the number and proportional area of rice panicles. Currently, rice ...
In this paper, UAV (unmanned aerial vehicle, DJI Phantom4RTK) and YOLOv4 (You Only Look Once) target...
Additional file 1: Video S1. The instruction of the rice panicle segmentation software.mp4
Additional file 5: Figure S1. Different regions in the field plot image have different illumination ...
Additional file 4: Table S2. The evaluation criterion for 48 testing rice samples using four differe...
Accurate and timely detection of phenology at plot scale in rice breeding trails is crucial for unde...