[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.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
An open-source software for field-based plant phenotyping,Precision Plots Analyzer(PREPs),was developed using Window.NET.The software runs on 64-bit Windows computers.This software allows the extraction of phenotypic ...An open-source software for field-based plant phenotyping,Precision Plots Analyzer(PREPs),was developed using Window.NET.The software runs on 64-bit Windows computers.This software allows the extraction of phenotypic traits on a per-microplot basis from orthomosaic and digital surface model(DSM)images generated by Structure-from-Motion/Multi-View-Stereo(SfM-MVS)tools.Moreover,there is no need to acquire skills in geographical information system(GIS)or programming languages for image analysis.Three use cases illustrated the software's functionality.The first involved monitoring the growth of sugar beet varieties in an experimental field using an unmanned aerial vehicle(UAV),where differences among varieties were detected through estimates of crop height,coverage,and volume index.Second,mixed varieties of potato crops were estimated using a UAV and varietal differences were observed from the estimated phenotypic traits.A strong correlation was observed between the manually measured crop height and UAV-estimated crop height.Finally,using a multicamera array attached to a tractor,the height,coverage,and volume index of the 3 potato varieties were precisely estimated.PREPs software is poised to be a useful tool that allows anyone without prior knowledge of programming to extract crop traits for phenotyping.展开更多
The measurement of banana pseudo-stem phenotypic parameters is a critical way to evaluate the growth status of bananas,and it can provide essential data support for mechanized cultivation operations such as fertilizat...The measurement of banana pseudo-stem phenotypic parameters is a critical way to evaluate the growth status of bananas,and it can provide essential data support for mechanized cultivation operations such as fertilization and pesticide application.Existing studies mainly measure the diameter of banana pseudo-stem as its phenotypic parameter.The banana pseudo-stem cross section was closer to an ellipse other than a standard circle,so the diameter parameter cannot adequately represent the phenotypic characteristics of the banana plant.In this study,an automatic measuring device for banana pseudo-stem phenotypic parameters was developed.The device,which integrates three different types of sensors:a laser ranging sensor,a rotary encoder,and a digital camera,were used to obtain the point cloud and image data of banana pseudo-stem.A K-means point clouds clustering algorithm based on Euclidean distance was proposed.The point cloud of banana pseudo-stem was identified and extracted.A three-dimensional reconstruction algorithm based on the ellipse model was also proposed.The three-dimensional contour of the pseudo-stem was calculated to obtain three types of phenotypic parameters:the long axis length,the short axis length,and the perimeter.Further,a synchronous trigger image acquisition mechanism was used to take pictures of pseudo-stems during measurement.It can be utilized for manual assessment of the growth status of the banana.Field experimental results showed that the three banana phenotypic parameters had a high correlation with the manual measurement results,and R^(2)is always more significant than 0.95,the total average measurement error and relative error were only 6.16 mm and 4.38%,respectively,both are within the acceptable agronomy range.In general,this method has good universality for plant stem detection,and the stem phenotypic parameters can be obtained by means of non-contact test,which is of great significance to the mechanized cultivation of the forest and fruit industry.展开更多
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
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.
基金partially supported by CREST(JPMJCR1512)AIP Acceleration Research(JPMJCR21U3)of JST.
文摘An open-source software for field-based plant phenotyping,Precision Plots Analyzer(PREPs),was developed using Window.NET.The software runs on 64-bit Windows computers.This software allows the extraction of phenotypic traits on a per-microplot basis from orthomosaic and digital surface model(DSM)images generated by Structure-from-Motion/Multi-View-Stereo(SfM-MVS)tools.Moreover,there is no need to acquire skills in geographical information system(GIS)or programming languages for image analysis.Three use cases illustrated the software's functionality.The first involved monitoring the growth of sugar beet varieties in an experimental field using an unmanned aerial vehicle(UAV),where differences among varieties were detected through estimates of crop height,coverage,and volume index.Second,mixed varieties of potato crops were estimated using a UAV and varietal differences were observed from the estimated phenotypic traits.A strong correlation was observed between the manually measured crop height and UAV-estimated crop height.Finally,using a multicamera array attached to a tractor,the height,coverage,and volume index of the 3 potato varieties were precisely estimated.PREPs software is poised to be a useful tool that allows anyone without prior knowledge of programming to extract crop traits for phenotyping.
基金This work was financially supported by the Laboratory of Lingnan Modern Agriculture Project(Grant No.NT2021009)the National Key Research and Development Program of China(Grant No.2020YFD1000104)+2 种基金the China Agriculture Research System of MOF and MARA(Grant No.CARS-31-10)the Key-Areas Research and Development Program of Guangdong Province,China(Grant No.2019B020223002)the Department of Education Special Program of Guangdong Province,China(Grant No.2020KZDZX1036).
文摘The measurement of banana pseudo-stem phenotypic parameters is a critical way to evaluate the growth status of bananas,and it can provide essential data support for mechanized cultivation operations such as fertilization and pesticide application.Existing studies mainly measure the diameter of banana pseudo-stem as its phenotypic parameter.The banana pseudo-stem cross section was closer to an ellipse other than a standard circle,so the diameter parameter cannot adequately represent the phenotypic characteristics of the banana plant.In this study,an automatic measuring device for banana pseudo-stem phenotypic parameters was developed.The device,which integrates three different types of sensors:a laser ranging sensor,a rotary encoder,and a digital camera,were used to obtain the point cloud and image data of banana pseudo-stem.A K-means point clouds clustering algorithm based on Euclidean distance was proposed.The point cloud of banana pseudo-stem was identified and extracted.A three-dimensional reconstruction algorithm based on the ellipse model was also proposed.The three-dimensional contour of the pseudo-stem was calculated to obtain three types of phenotypic parameters:the long axis length,the short axis length,and the perimeter.Further,a synchronous trigger image acquisition mechanism was used to take pictures of pseudo-stems during measurement.It can be utilized for manual assessment of the growth status of the banana.Field experimental results showed that the three banana phenotypic parameters had a high correlation with the manual measurement results,and R^(2)is always more significant than 0.95,the total average measurement error and relative error were only 6.16 mm and 4.38%,respectively,both are within the acceptable agronomy range.In general,this method has good universality for plant stem detection,and the stem phenotypic parameters can be obtained by means of non-contact test,which is of great significance to the mechanized cultivation of the forest and fruit industry.
