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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://gffzzef49fa24bca249bbhovcwuonu6vuo6c0v.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 | 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 |
| 3 | Latent Space Phenotyping:Automatic Image-Based Phenotyping for Treatment Studies显示文摘Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors,including agronomically relevant tolerance traits in plants.However,traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses,typically in an automated highthroughput context using image processing.In this work,we present Latent Space Phenotyping(LSP),a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images.We demonstrate example applications using data from an interspecific cross of the model C_(4) grass Setaria,a diversity panel of sorghum(S.bicolor),and the founder panel for a nested association mapping population of canola(Brassica napus L.).Using two synthetically generated image datasets,we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery.We propose LSP as an alternative to traditional image analysis methods for phenotyping,enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines. | Jordan Ubbens Mikolaj Cieslak Przemyslaw Prusinkiewicz Isobel Parkin Jana Ebersbach Ian Stavness | 2020 | Plant Phenomics2020，2，1： | 1 |
| 4 | Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method显示文摘To develop new crop varieties and monitor plant growth,health,and traits,automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection.To perform per-microplot phenotypic analysis,localizing and detecting individual microplots in an orthomosaic image of a field are major steps.Our algorithm uses an automatic initialization of the known field layout over the orthomosaic images in roughly the right position.Since the orthomosaic images are stitched from a large number of smaller images,there can be distortion causing microplot rows not to be entirely straight and the automatic initialization to not correctly position every microplot.To overcome this,we have developed a three-level hierarchical optimization method.First,the initial bounding box position is optimized using an objective function that maximizes the level of vegetation inside the area.Then,columns of microplots are repositioned,constrained by their expected spacing.Finally,the position of microplots is adjusted individually using an objective function that simultaneously maximizes the area of the microplot overlapping vegetation,minimizes spacing variance between microplots,and maximizes each microplot’s alignment relative to other microplots in the same row and column.The orthomosaics used in this study were obtained from multiple dates of canola and wheat breeding trials.The algorithm was able to detect 99.7%of microplots for canola and 99%for wheat.The automatically segmented microplots were compared to ground truth segmentations,resulting in an average DSC of 91.2%and 89.6%across all microplots and orthomosaics in the canola and wheat datasets. | Sara Mardanisamani Tewodros WAyalew Minhajul Arifin Badhon Nazifa Azam Khan Gazi Hasnat Hema Duddu Steve Shirtliffe Sally Vail Ian Stavness Mark Eramian | 2021 | Plant Phenomics2021，3，1： | 0 |
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