Fusiform rust,caused by the pathogen Cronartium quercuum(Berk.)Miyabe ex Shirai f.sp.fusiforme,is the most important disease of loblolly pine(Pinus taeda L.)in the U.S.,causing millions of dollars in damage each year....Fusiform rust,caused by the pathogen Cronartium quercuum(Berk.)Miyabe ex Shirai f.sp.fusiforme,is the most important disease of loblolly pine(Pinus taeda L.)in the U.S.,causing millions of dollars in damage each year.Using resistant genotypes has proven a successful strategy to limit the disease,but resistance selection still relies on visual inspection for symptoms,which can lead to misclassification due to human error and the presence of'escaped susceptibles'(i.e.,susceptible individuals with no visible symptoms due to either an extended asymp-tomatic phase of the disease or the lack of adequate disease pressure to become infected).Here,we propose the use of near-infrared(NIR)spectroscopy and chemometrics to improve the accuracy of how phenotypes are rated.We collected and analyzed phloem and needle spectra from 34 non-related families replicated across eight stands in three states in the southeastern region of the U.S.using a portable,handheld NIR spectrometer.We also used a benchtop Fourier-transformed mid-infrared(FT-IR)spectrometer to analyze phloem phenolic extracts of the same samples,as this phenotyping approach has proved successful in other pathosystems.Our results show a moderate association between the phloem spectra and resistance,and models built with NIR spectra were able to classify extremes(i.e.,very resistant or very susceptible)with up to 69%testing accuracy.This study provides a framework for using NIR spectroscopy for phenotyping loblolly pine resistance against pathogens and advocates for using alternative technologies in forestry.展开更多
Early detection of plant diseases,prior to symptom development,can allow for targeted and more proactive disease management.The objective of this study was to evaluate the use of near-infrared(NIR)spectroscopy combine...Early detection of plant diseases,prior to symptom development,can allow for targeted and more proactive disease management.The objective of this study was to evaluate the use of near-infrared(NIR)spectroscopy combined with machine learning for early detection of rice sheath blight(ShB),caused by the fungus Rhizoctonia solani.We collected NIR spectra from leaves of ShBsusceptible rice(Oryza sativa L.)cultivar,Lemont,growing in a growth chamber one day following inoculation with R.solani,and prior to the development of any disease symptoms.Support vector machine(SVM)and random forest,two machine learning algorithms,were used to build and evaluate the accuracy of supervised classification-based disease predictive models.Sparse partial least squares discriminant analysis was used to confirm the results.The most accurate model comparing mockinoculated and inoculated plants was SVM-based and had an overall testing accuracy of 86.1%(N=72),while when control,mock-inoculated,and inoculated plants were compared the most accurate SVM model had an overall testing accuracy of 73.3%(N=105).These results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development.While testing and validation in field trials are still needed,this technique holds promise for application in the field for disease diagnosis and management.展开更多
Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in...Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.展开更多
基金This research was funded by the United States Forest Service,Forest Health Protection Special Technology Development Program(grant number 20-DG-11083150-003)the Southern Pine Health Research Cooperative(SPHRC)at the University of Georgia(Athens,Georgia,United States).
文摘Fusiform rust,caused by the pathogen Cronartium quercuum(Berk.)Miyabe ex Shirai f.sp.fusiforme,is the most important disease of loblolly pine(Pinus taeda L.)in the U.S.,causing millions of dollars in damage each year.Using resistant genotypes has proven a successful strategy to limit the disease,but resistance selection still relies on visual inspection for symptoms,which can lead to misclassification due to human error and the presence of'escaped susceptibles'(i.e.,susceptible individuals with no visible symptoms due to either an extended asymp-tomatic phase of the disease or the lack of adequate disease pressure to become infected).Here,we propose the use of near-infrared(NIR)spectroscopy and chemometrics to improve the accuracy of how phenotypes are rated.We collected and analyzed phloem and needle spectra from 34 non-related families replicated across eight stands in three states in the southeastern region of the U.S.using a portable,handheld NIR spectrometer.We also used a benchtop Fourier-transformed mid-infrared(FT-IR)spectrometer to analyze phloem phenolic extracts of the same samples,as this phenotyping approach has proved successful in other pathosystems.Our results show a moderate association between the phloem spectra and resistance,and models built with NIR spectra were able to classify extremes(i.e.,very resistant or very susceptible)with up to 69%testing accuracy.This study provides a framework for using NIR spectroscopy for phenotyping loblolly pine resistance against pathogens and advocates for using alternative technologies in forestry.
基金The authors thank Lauren Schnieky,Caleb Kime,Carrie Ewing,and Soumya Ghosh for assistance in collecting spec-tral data.Funding for this project was provided by a Grand Challenges Exploration Grant from the Bill and Melinda Gates Foundation award ID OPP1199430.
文摘Early detection of plant diseases,prior to symptom development,can allow for targeted and more proactive disease management.The objective of this study was to evaluate the use of near-infrared(NIR)spectroscopy combined with machine learning for early detection of rice sheath blight(ShB),caused by the fungus Rhizoctonia solani.We collected NIR spectra from leaves of ShBsusceptible rice(Oryza sativa L.)cultivar,Lemont,growing in a growth chamber one day following inoculation with R.solani,and prior to the development of any disease symptoms.Support vector machine(SVM)and random forest,two machine learning algorithms,were used to build and evaluate the accuracy of supervised classification-based disease predictive models.Sparse partial least squares discriminant analysis was used to confirm the results.The most accurate model comparing mockinoculated and inoculated plants was SVM-based and had an overall testing accuracy of 86.1%(N=72),while when control,mock-inoculated,and inoculated plants were compared the most accurate SVM model had an overall testing accuracy of 73.3%(N=105).These results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development.While testing and validation in field trials are still needed,this technique holds promise for application in the field for disease diagnosis and management.
文摘Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.
