The first paradigm of plant breeding involves direct selection-based phenotypic observation,followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental de...The first paradigm of plant breeding involves direct selection-based phenotypic observation,followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and,more recently,by incorporation of molecular marker genotypes.However,plant performance or phenotype(P)is determined by the combined effects of genotype(G),envirotype(E),and genotype by environment interaction(GEI).Phenotypes can be predicted more precisely by training a model using data collected from multiple sources,including spatiotemporal omics(genomics,phenomics,and enviromics across time and space).Integration of 3D information profiles(G-P-E),each with multidimensionality,provides predictive breeding with both tremendous opportunities and great challenges.Here,we first review innovative technologies for predictive breeding.We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy,particularly envirotypic data,which have largely been neglected in data collection and are nearly untouched in model construction.We propose a smart breeding scheme,integrated genomic-enviromic prediction(iGEP),as an extension of genomic prediction,using integrated multiomics information,big data technology,and artificial intelligence(mainly focused on machine and deep learning).We discuss how to implement iGEP,including spatiotemporal models,environmental indices,factorial and spatiotemporal structure of plant breeding data,and cross-species prediction.A strategy is then proposed for prediction-based crop redesign at both the macro(individual,population,and species)and micro(gene,metabolism,and network)scales.Finally,we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives.We call for coordinated efforts in smart breeding through iGEP,institutional partnerships,and innovative technological support.展开更多
In the face of climate change and the growing global population,there is an urgent need to accelerate the development of high-yielding crop varieties.To this end,vast amounts of genotype-to-phenotype data have been co...In the face of climate change and the growing global population,there is an urgent need to accelerate the development of high-yielding crop varieties.To this end,vast amounts of genotype-to-phenotype data have been collected,and many machine learning(ML)models have been developed to predict phenotype from a given genotype.However,the requirement for high densities of single-nucleotide polymorphisms(SNPs)and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding.Furthermore,recently developed genomic selection(GS)models,such as deep learning(DL),are complicated and inconvenient for breeders to navigate and optimize within their breeding programs.Here,we present the development of an intelligent breeding platform named AutoGP(http://autogp.hzau.edu.cn),which integrates genotype extraction,phenotypic extraction,and GS models of genotype-to-phenotype data within a user-friendly web interface.AutoGP has three main advantages over previously developed platforms:1)an efficient sequencing chip to identify high-quality,high-confidence SNPs throughout gene-regulatory networks;2)a complete workflow for extraction of plant phenotypes(such as plant height and leaf count)from smartphone-captured video;and 3)a broad model pool,enabling users to select from five ML models(support vector machine,extreme gradient boosting,gradient-boosted de-cision tree,multilayer perceptron,and random forest)and four commonly used DL models(deep learning genomic selection,deep learning genomic-wide association study,deep neural network for genomic pre-diction,and SoyDNGP).For the convenience of breeders,we use datasets from the maize(Zea mays)com-plete-diallel design plus unbalanced breeding-like inter-cross population as a case study to demonstrate the usefulness of AutoGP.We show that our genotype chips can effectively extract high-quality SNPs asso-ciated with days to tasseling and plant height.The models show reliable predictive accuracy on different populations and can provide effective guidance for breeders.Overall,AutoGP offers a practical solution to streamline the breeding process,enabling breeders to achieve more efficient and accurate genomic selection.展开更多
[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of ...[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of body morphometrics is critical for assessing growth performance and breeding value.Traditional manual measurements are inefficient,prone to human error,and may cause stress to sheep,limiting their suitability for precision sheep management.By summarizing the applications of sheep body size measurement technologies and analyzing their development directions,this paper provides theoretical references and practical guidance for the research and application of non contact sheep body size measurement.[Progress]This review synthesizes progress across three principal methodological paradigms:two-dimensional(2D)image-based techniques,three-dimensional(3D)point cloud-based approaches,and integrated 2D-3D fusion systems.2D methods,employing either handcrafted geometric features or deep learning-based keypoint detector algorithms,are cost-effective and operationally simple but sensitive to variation in imaging conditions and unable to capture critical circumference metrics.3D point-cloud approaches enable precise reconstruction of full animal morphology,supporting comprehensive body-size acquisition with higher accuracy,yet face challenges including high hardware costs,complex data workflows,and sensitivity to posture variability.Hybrid 2D-3D fusion systems combine semantic richness from RGB imagery with geometric completeness from point clouds.Having been effectively validated in other livestock specise,e.g.,cattle and pigs,these fusion systems have demonstrated excellent performance,providing important technical references and practical insights for sheep body size measurement.[Conclusions and Prospects]Firstly,future research should focus on constructing large-scale,high-quality datasets for sheep body size measurement that encompass diverse breeds,growth stages,and environmental conditions,thereby enhancing model robustness and generalization.Secondly,the development of lightweight artificial intelligence models is essential.Techniques such as model compression,quantization,and algorithmic optimization can substantially reduce computational complexity and storage requirements,facilitating deployment in resource-constrained environments.Thirdly,the 3D point cloud processing pipeline should be streamlined to improve the efficiency of data acquisition,filtering,registration,and segmentation,while promoting the integration of low-cost,high-resilience vision systems into practical farming scenarios.Fourthly,specific emphasis should be placed on improving the accuracy of curved-dimensional measurements,such as chest circumference,abdominal circumference,and shank circumference,through advances in pose standardization,refined 3D segmentation strategies,and multimodal data fusion.Finally,the cross-fertilization of sheep body size measurement technologies with analogous methods for other livestock species offers a promising pathway for mutual learning and collaborative innovation,accelerating the industrialization of automated sheep morphometric systems and supporting the development of intelligent,data-driven pasture management practices.展开更多
Fusarium Head Blight(FHB),a fungal wheat(Triticum aestivum)disease that threatens global food security,requires precise quantification of diseased spikelet rate(DSR)as a phenotypic indicator for resistance breeding.Mo...Fusarium Head Blight(FHB),a fungal wheat(Triticum aestivum)disease that threatens global food security,requires precise quantification of diseased spikelet rate(DSR)as a phenotypic indicator for resistance breeding.Most techniques for measuring DSR rely on manual spikelet-by-spikelet observation and counting,which is inefficient and destructive.Although deep learning offers great promise for automated DSR measurement,existing intelligent detection algorithms are hampered by the lack of spikelet-level annotated data,insufficient feature representation for diseased spikelets,and weak spatial encoding of densely arranged spikelets.To address these challenges,we constructed a dataset of 620 high-resolution RGB images of wheat spikes with 5,222 spikelet-level annotations to systematically analyze spikelet size distributions to fill small-object detection data gaps in this field.We designed FHBDSR-Net,a light framework for automated DSR measurement centered on diseased spikelet detection,which features(1)multi-scale feature enhancement architecture that dynamically combines lesion textures,morphological features,and lesion-awn contrast through adaptive multi-scale kernels to suppress background noise;(2)the Inner-EfficiCIoU loss function to reduce small-target localization errors in dense contexts;and(3)a scale-aware attention module using dilated convolutions and selfattention to encode multi-scale pathological patterns and spatial distributions to enhance dense spikelet resolution.FHBDSR-Net detected diseased spikelets with an average precision of 93.8%with a lightweight design of 7.2 M parameters.The results were strongly correlated with expert evaluations,with a Pearson correlation coefficient of 0.901.Our method is suitable for deployment on resourceconstrained mobile devices,facilitating portable plant phenotyping and smart breeding.展开更多
基金National Key Research and Development Program of China(2016YFD0101803)Central Public-interest Scientific Institution Basal Research Fund(Y2020PT20)+5 种基金Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS-XTCX2016009)Shijiazhuang Science and Technology Incubation Program(191540089A)Hebei Innovation Capability Enhancement Project(19962911D)Project of Hainan Yazhou Bay Seed Laboratory(B21HJ0223)Department of Science and Technology of Ninxia Project(NXNYYZ202001)Research activities at CIMMYT were supported by the Bill and Melinda Gates Foundation and the CGIAR Research Program MAIZE.
文摘The first paradigm of plant breeding involves direct selection-based phenotypic observation,followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and,more recently,by incorporation of molecular marker genotypes.However,plant performance or phenotype(P)is determined by the combined effects of genotype(G),envirotype(E),and genotype by environment interaction(GEI).Phenotypes can be predicted more precisely by training a model using data collected from multiple sources,including spatiotemporal omics(genomics,phenomics,and enviromics across time and space).Integration of 3D information profiles(G-P-E),each with multidimensionality,provides predictive breeding with both tremendous opportunities and great challenges.Here,we first review innovative technologies for predictive breeding.We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy,particularly envirotypic data,which have largely been neglected in data collection and are nearly untouched in model construction.We propose a smart breeding scheme,integrated genomic-enviromic prediction(iGEP),as an extension of genomic prediction,using integrated multiomics information,big data technology,and artificial intelligence(mainly focused on machine and deep learning).We discuss how to implement iGEP,including spatiotemporal models,environmental indices,factorial and spatiotemporal structure of plant breeding data,and cross-species prediction.A strategy is then proposed for prediction-based crop redesign at both the macro(individual,population,and species)and micro(gene,metabolism,and network)scales.Finally,we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives.We call for coordinated efforts in smart breeding through iGEP,institutional partnerships,and innovative technological support.
基金supported by Biological Breeding-National Science and Technology Major Project(2023ZD04076)the National Key Research and Development Program of China(2023YFF1000100)+2 种基金the National Natural Science Foundation of China(32321005 and 32261143463)the Fundamental Research Funds for the Central Universities of China(2662024XXPY001)the Outstanding Youth Team Cultivation Project of Center Universities(2662023PY007).
文摘In the face of climate change and the growing global population,there is an urgent need to accelerate the development of high-yielding crop varieties.To this end,vast amounts of genotype-to-phenotype data have been collected,and many machine learning(ML)models have been developed to predict phenotype from a given genotype.However,the requirement for high densities of single-nucleotide polymorphisms(SNPs)and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding.Furthermore,recently developed genomic selection(GS)models,such as deep learning(DL),are complicated and inconvenient for breeders to navigate and optimize within their breeding programs.Here,we present the development of an intelligent breeding platform named AutoGP(http://autogp.hzau.edu.cn),which integrates genotype extraction,phenotypic extraction,and GS models of genotype-to-phenotype data within a user-friendly web interface.AutoGP has three main advantages over previously developed platforms:1)an efficient sequencing chip to identify high-quality,high-confidence SNPs throughout gene-regulatory networks;2)a complete workflow for extraction of plant phenotypes(such as plant height and leaf count)from smartphone-captured video;and 3)a broad model pool,enabling users to select from five ML models(support vector machine,extreme gradient boosting,gradient-boosted de-cision tree,multilayer perceptron,and random forest)and four commonly used DL models(deep learning genomic selection,deep learning genomic-wide association study,deep neural network for genomic pre-diction,and SoyDNGP).For the convenience of breeders,we use datasets from the maize(Zea mays)com-plete-diallel design plus unbalanced breeding-like inter-cross population as a case study to demonstrate the usefulness of AutoGP.We show that our genotype chips can effectively extract high-quality SNPs asso-ciated with days to tasseling and plant height.The models show reliable predictive accuracy on different populations and can provide effective guidance for breeders.Overall,AutoGP offers a practical solution to streamline the breeding process,enabling breeders to achieve more efficient and accurate genomic selection.
文摘[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of body morphometrics is critical for assessing growth performance and breeding value.Traditional manual measurements are inefficient,prone to human error,and may cause stress to sheep,limiting their suitability for precision sheep management.By summarizing the applications of sheep body size measurement technologies and analyzing their development directions,this paper provides theoretical references and practical guidance for the research and application of non contact sheep body size measurement.[Progress]This review synthesizes progress across three principal methodological paradigms:two-dimensional(2D)image-based techniques,three-dimensional(3D)point cloud-based approaches,and integrated 2D-3D fusion systems.2D methods,employing either handcrafted geometric features or deep learning-based keypoint detector algorithms,are cost-effective and operationally simple but sensitive to variation in imaging conditions and unable to capture critical circumference metrics.3D point-cloud approaches enable precise reconstruction of full animal morphology,supporting comprehensive body-size acquisition with higher accuracy,yet face challenges including high hardware costs,complex data workflows,and sensitivity to posture variability.Hybrid 2D-3D fusion systems combine semantic richness from RGB imagery with geometric completeness from point clouds.Having been effectively validated in other livestock specise,e.g.,cattle and pigs,these fusion systems have demonstrated excellent performance,providing important technical references and practical insights for sheep body size measurement.[Conclusions and Prospects]Firstly,future research should focus on constructing large-scale,high-quality datasets for sheep body size measurement that encompass diverse breeds,growth stages,and environmental conditions,thereby enhancing model robustness and generalization.Secondly,the development of lightweight artificial intelligence models is essential.Techniques such as model compression,quantization,and algorithmic optimization can substantially reduce computational complexity and storage requirements,facilitating deployment in resource-constrained environments.Thirdly,the 3D point cloud processing pipeline should be streamlined to improve the efficiency of data acquisition,filtering,registration,and segmentation,while promoting the integration of low-cost,high-resilience vision systems into practical farming scenarios.Fourthly,specific emphasis should be placed on improving the accuracy of curved-dimensional measurements,such as chest circumference,abdominal circumference,and shank circumference,through advances in pose standardization,refined 3D segmentation strategies,and multimodal data fusion.Finally,the cross-fertilization of sheep body size measurement technologies with analogous methods for other livestock species offers a promising pathway for mutual learning and collaborative innovation,accelerating the industrialization of automated sheep morphometric systems and supporting the development of intelligent,data-driven pasture management practices.
基金supported by the National Natural Science Foundation of China(grant nos.32200331 and U24A20344).
文摘Fusarium Head Blight(FHB),a fungal wheat(Triticum aestivum)disease that threatens global food security,requires precise quantification of diseased spikelet rate(DSR)as a phenotypic indicator for resistance breeding.Most techniques for measuring DSR rely on manual spikelet-by-spikelet observation and counting,which is inefficient and destructive.Although deep learning offers great promise for automated DSR measurement,existing intelligent detection algorithms are hampered by the lack of spikelet-level annotated data,insufficient feature representation for diseased spikelets,and weak spatial encoding of densely arranged spikelets.To address these challenges,we constructed a dataset of 620 high-resolution RGB images of wheat spikes with 5,222 spikelet-level annotations to systematically analyze spikelet size distributions to fill small-object detection data gaps in this field.We designed FHBDSR-Net,a light framework for automated DSR measurement centered on diseased spikelet detection,which features(1)multi-scale feature enhancement architecture that dynamically combines lesion textures,morphological features,and lesion-awn contrast through adaptive multi-scale kernels to suppress background noise;(2)the Inner-EfficiCIoU loss function to reduce small-target localization errors in dense contexts;and(3)a scale-aware attention module using dilated convolutions and selfattention to encode multi-scale pathological patterns and spatial distributions to enhance dense spikelet resolution.FHBDSR-Net detected diseased spikelets with an average precision of 93.8%with a lightweight design of 7.2 M parameters.The results were strongly correlated with expert evaluations,with a Pearson correlation coefficient of 0.901.Our method is suitable for deployment on resourceconstrained mobile devices,facilitating portable plant phenotyping and smart breeding.
