Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of p...Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features,including size,shape,and colour.Although today's AI-driven foundation models segment almost any object in an image,they still fail for complex plant canopies.To improve model performance,the global wheat dataset consortium assembled a diverse set of images from experiments around the globe.After the head detection dataset(GWHD),the new dataset targets a full semantic segmentation(GWFSS)of organs(leaves,stems and spikes)covering all developmental stages.Images were collected by 11 institutions using a wide range of imaging setups.Two datasets are provided:ⅰ)a set of 1096 diverse images in which all organs were labelled at the pixel level,and(ⅱ)a dataset of 52,078 images without annotations available for additional training.The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca.90%.However,the precision for stems with 54%was rather lower.The major advantages over published models are:ⅰ)the exclusion of weeds from the wheat canopy,ⅱ)the detection of all wheat features including necrotic and se-nescent tissues and its separation from crop residues.This facilitates further development in classifying healthy vs.unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.展开更多
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 ass...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.展开更多
The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different organs.Recent deep learning algorithms appear as prom...The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different organs.Recent deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of conditions.However,they remain complicated to implement and necessitate a huge training database.This paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage.展开更多
基金Global wheat was directly supported by Analytics for the Australian Grains Industry(AAGI).
文摘Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features,including size,shape,and colour.Although today's AI-driven foundation models segment almost any object in an image,they still fail for complex plant canopies.To improve model performance,the global wheat dataset consortium assembled a diverse set of images from experiments around the globe.After the head detection dataset(GWHD),the new dataset targets a full semantic segmentation(GWFSS)of organs(leaves,stems and spikes)covering all developmental stages.Images were collected by 11 institutions using a wide range of imaging setups.Two datasets are provided:ⅰ)a set of 1096 diverse images in which all organs were labelled at the pixel level,and(ⅱ)a dataset of 52,078 images without annotations available for additional training.The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca.90%.However,the precision for stems with 54%was rather lower.The major advantages over published models are:ⅰ)the exclusion of weeds from the wheat canopy,ⅱ)the detection of all wheat features including necrotic and se-nescent tissues and its separation from crop residues.This facilitates further development in classifying healthy vs.unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.
基金the French National Research Agency under the Investments for the Future Program,referred as ANR-16-CONV-0004 PIA#Digitag.Institut Convergences Agriculture Numérique,Hiphen supported the organization of the competition.Japan:Kubota supported the organization of the competi-tion.Australia:Grains Research and Development Corpora-tion(UOQ2002-008RTX machine learning applied to high-throughput feature extraction from imagery to map spatial variability and UOQ2003-011RTX INVITA-a technology and analytics platform for improving variety selection)sup-ported competition.
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.
文摘The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different organs.Recent deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of conditions.However,they remain complicated to implement and necessitate a huge training database.This paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage.
