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
Red lentils may have either a reddish-brown or grey seed coat with different pigmentation patterns,and coty-ledons varying from yellow to orange-red depending on the genotype.This class of lentils is widely consumed f...Red lentils may have either a reddish-brown or grey seed coat with different pigmentation patterns,and coty-ledons varying from yellow to orange-red depending on the genotype.This class of lentils is widely consumed for their nutritional and functional properties the latter being largely attributed to antioxidant polyphenols.How-ever,the relationship between seed morphology and polyphenol composition remains poorly characterized.In this study,59 red lentil genotypes were analyzed to investigate associations among seed color,polyphenol profiles,and antioxidant capacity.Total phenolic content and individual flavonoids were quantified,and anti-oxidant activity was assessed both in extracts and directly on whole-meal flour.ANOVA revealed significant variability among genotypes for all traits.Three-way ANOVA highlighted that different tegument pigmentation patterns were strongly associated with individual flavonoids and antioxidant capacity,while cotyledon color was influenced by total polyphenol content and gallic acid levels.Multivariate analyses(PCA,OPLS-DA)confirmed these relationships by achieving high classification accuracy for color classes based on combined biochemical and physical data.Overall,the results showed that morphological features of red lentils can be valuable tools for accelerating breeding programs for selecting and developing varieties rich in functional compounds.展开更多
This study aimed to investigate the biochemical basis of seed morphological traits in red lentils that are important for lentil producers in relation to quality,consumers’preferences and commercial value.To achieve t...This study aimed to investigate the biochemical basis of seed morphological traits in red lentils that are important for lentil producers in relation to quality,consumers’preferences and commercial value.To achieve this objective,proton Nuclear Magnetic Resonance(^(1)H NMR)spectroscopy combined with multivariate statistical analyses was employed.A collection of 64 red lentil varieties exhibiting diversity in seed colour,size,weight,and cotyledon pigmentation was analysed.Aqueous extracts of the seeds were profiled using^(1)H NMR,and spectra were processed into bucketed variables.Partial Least Squares Regression and Multiple Linear Regression were applied to assess relationships between spectral data and continuous morphological traits:lightness(L^(*)),chromatic indexes(a^(*),b^(*)),Hundred Kernel Weight,and seed size.For categorical traits like cotyledon colour,Partial Least Squares Discriminant Analysis(PLS-DA)and binomial logistic regression were used.Variable Importance in Projection scores helped to identify key metabolite buckets significantly contributing to trait prediction.Metabolites such as leucine,fructose,and phenolic compounds were positively associated with seed size and weight,while NAD+and short-chain fatty acids showed negative associations.Cotyledon colour classification achieved high accuracy(up to 100%)using both PLS-DA and logistic models,with amino acids like leucine and alanine linked to yellow pigmentation and tryptophan and citrate linked to orange.Overall,the study demonstrates that^(1)H NMR fingerprinting,combined with rigorous statistical modelling,effectively elucidates the multivariate relationships between metabolomic profiles and key agronomic traits,providing a valuable tool for phenotypic prediction and lentil breeding.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funded by the following projects:PRIN2022 project acronyms GRANARIUS(Project Nr.202282ZTPL)and LENTIGO(Project Nr.2022SFMSW3)Agritech National Research Center,receiving funding from the European Union Next-Generation EU(PIANO NAZIONALE DI RIPRESA E RESILIENZA(PNRR)-MISSIONE 4 COMPONENTE 2,INVESTIMENTO 1.4-D.D.1032 June 17,2022,CN00000022)support from Dr.Simonetta Martena and Marinella Cavallo for GRAN-ARIUS Project management.
文摘Red lentils may have either a reddish-brown or grey seed coat with different pigmentation patterns,and coty-ledons varying from yellow to orange-red depending on the genotype.This class of lentils is widely consumed for their nutritional and functional properties the latter being largely attributed to antioxidant polyphenols.How-ever,the relationship between seed morphology and polyphenol composition remains poorly characterized.In this study,59 red lentil genotypes were analyzed to investigate associations among seed color,polyphenol profiles,and antioxidant capacity.Total phenolic content and individual flavonoids were quantified,and anti-oxidant activity was assessed both in extracts and directly on whole-meal flour.ANOVA revealed significant variability among genotypes for all traits.Three-way ANOVA highlighted that different tegument pigmentation patterns were strongly associated with individual flavonoids and antioxidant capacity,while cotyledon color was influenced by total polyphenol content and gallic acid levels.Multivariate analyses(PCA,OPLS-DA)confirmed these relationships by achieving high classification accuracy for color classes based on combined biochemical and physical data.Overall,the results showed that morphological features of red lentils can be valuable tools for accelerating breeding programs for selecting and developing varieties rich in functional compounds.
基金funded by PRIMA Section 12020 Agrofood Value ChainIA Topic:1.3.1-2020(IA),MEDWHEALTH,project grant n°2034by PRIN2022 GRANARIUS,MIUR D.D.n.976 July 3,2023,project grant n°202282ZTPL.
文摘This study aimed to investigate the biochemical basis of seed morphological traits in red lentils that are important for lentil producers in relation to quality,consumers’preferences and commercial value.To achieve this objective,proton Nuclear Magnetic Resonance(^(1)H NMR)spectroscopy combined with multivariate statistical analyses was employed.A collection of 64 red lentil varieties exhibiting diversity in seed colour,size,weight,and cotyledon pigmentation was analysed.Aqueous extracts of the seeds were profiled using^(1)H NMR,and spectra were processed into bucketed variables.Partial Least Squares Regression and Multiple Linear Regression were applied to assess relationships between spectral data and continuous morphological traits:lightness(L^(*)),chromatic indexes(a^(*),b^(*)),Hundred Kernel Weight,and seed size.For categorical traits like cotyledon colour,Partial Least Squares Discriminant Analysis(PLS-DA)and binomial logistic regression were used.Variable Importance in Projection scores helped to identify key metabolite buckets significantly contributing to trait prediction.Metabolites such as leucine,fructose,and phenolic compounds were positively associated with seed size and weight,while NAD+and short-chain fatty acids showed negative associations.Cotyledon colour classification achieved high accuracy(up to 100%)using both PLS-DA and logistic models,with amino acids like leucine and alanine linked to yellow pigmentation and tryptophan and citrate linked to orange.Overall,the study demonstrates that^(1)H NMR fingerprinting,combined with rigorous statistical modelling,effectively elucidates the multivariate relationships between metabolomic profiles and key agronomic traits,providing a valuable tool for phenotypic prediction and lentil breeding.
