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您的检索式：作者名="Benoit de Solan"
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| 1 | 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显示文摘The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health,size,maturity stage,and the presence of awns.Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms.However,these methods have generally been calibrated and validated on limited datasets.High variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer vision.Further,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex.Through a joint international collaborative effort,we have built a large,diverse,and well-labelled dataset of wheat images,called the Global Wheat Head Detection(GWHD)dataset.It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes.Guidelines for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection datasets.The GWHD dataset is publicly available at http://gffzzef49fa24bca249bbhbc655pvbp0f56bo6.ffgz.tsg.suse.edu.cn/and aimed at developing and benchmarking methods for wheat head detection. | Etienne David Simon Madec Pouria Sadeghi-Tehran Helge Aasen Bangyou Zheng Shouyang Liu Norbert Kirchgessner Goro Ishikawa Koichi Nagasawa Minhajul A.Badhon Curtis Pozniak Benoit de Solan Andreas Hund Scott C.Chapman Frédéric Baret Ian Stavness Wei Guo | 2020 | Plant Phenomics2020，2，1： | 13 |
| 2 | High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass显示文摘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 nitrogen treatments.A high spatial resolution RGB camera was used to capture the residual stems standing straight after the cutting by the combine machine during harvest.It provided a ground spatial resolution better than 0.2 mm.A Faster Regional Convolutional Neural Network(Faster-RCNN)deep-learning model was first trained to identify the stems cross section.Results showed that the identification provided precision and recall close to 95%.Further,the balance between precision and recall allowed getting accurate estimates of the stem density with a relative RMSE close to 7%and robustness across the two experimental sites.The estimated stem density was also compared with the ear density measured in the field with traditional methods.A very high correlation was found with almost no bias,indicating that the stem density could be a good proxy of the ear density.The heritability/repeatability evaluated over 16 genotypes in one of the two experiments was slightly higher(80%)than that of the ear density(78%).The diameter of each stem was computed from the profile of gray values in the extracts of the stem cross section.Results show that the stem diameters follow a gamma distribution over eachmicroplot with an average diameter close to 2.0mm.Finally,the biovolume computed as the product of the average stem diameter,the stem density,and plant height is closely related to the above-ground biomass at harvest with a relative RMSE of 6%.Possible limitations of the findings and future applications are finally discussed. | Xiuliang Jin Simon Madec Dan Dutartre Benoit de Solan Alexis Comar Frédéric Baret | 2019 | Plant Phenomics2019，1，1： | 5 |
| 3 | Global Wheat Head Detection 2021:An Improved Dataset for Benchmarking Wheat Head Detection Methods显示文摘The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions.With an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities.From this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label reliability.To address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat heads.We now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version. | Etienne David Mario Serouart Daniel Smith Simon Madec Kaaviya Velumani Shouyang Liu Xu Wang Francisco Pinto Shahameh Shafiee Izzat SATahir Hisashi Tsujimoto Shuhei Nasuda Bangyou Zheng Norbert Kirchgessner Helge Aasen Andreas Hund Pouria Sadhegi-Tehran Koichi Nagasawa Goro Ishikawa Sébastien Dandrifosse Alexis Carlier Benjamin Dumont Benoit Mercatoris Byron Evers Ken Kuroki Haozhou Wang Masanori Ishii Minhajul ABadhon Curtis Pozniak David Shaner LeBauer Morten Lillemo Jesse Poland Scott Chapman Benoit de Solan Frédéric Baret Ian Stavness Wei Guo | 2021 | Plant Phenomics2021，3，1： | 2 |
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