Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease
暂无分享,去创建一个
Achim Walter | Jonas Anderegg | Andreas Hund | Bruce A. McDonald | Kang Yu | A. Walter | A. Hund | B. McDonald | Kang Yu | P. Karisto | F. Mascher | Jonas Anderegg | A. Mikaberidze | Alexey Mikaberidze | Petteri Karisto | Fabio Mascher | K. Yu | Alexey Mikaberidze | Petteri Karisto
[1] R. M. Rivero,et al. Delayed leaf senescence induces extreme drought tolerance in a flowering plant , 2007, Proceedings of the National Academy of Sciences.
[2] Rainer Laudien,et al. ANALYSIS OF HYPERSPECTRAL FIELD DATA FOR DETECTION OF SUGAR BEET DISEASES , 2003 .
[3] Achim Walter,et al. The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. , 2016, Functional plant biology : FPB.
[4] Sreekala G. Bajwa,et al. Soybean Disease Monitoring with Leaf Reflectance , 2017, Remote. Sens..
[5] R. Singh,et al. Seedling and Slow Rusting Resistance to Stripe Rust in Chinese Common Wheats. , 2006, Plant disease.
[6] D. Sims,et al. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .
[7] M. S. Moran,et al. Remote Sensing for Crop Management , 2003 .
[8] John R. Miller,et al. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..
[9] S. Torriani,et al. Zymoseptoria tritici: A major threat to wheat production, integrated approaches to control. , 2015, Fungal genetics and biology : FG & B.
[10] A. Gitelson,et al. Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening , 1999 .
[11] M. McHugh. Interrater reliability: the kappa statistic , 2012, Biochemia medica.
[12] Anne-Katrin Mahlein. Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. , 2016, Plant disease.
[13] J. Gamon,et al. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels , 1997, Oecologia.
[14] David W. Hosmer,et al. Applied Logistic Regression , 1991 .
[15] Won Suk Lee,et al. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees , 2013 .
[16] S. Wold,et al. PLS-regression: a basic tool of chemometrics , 2001 .
[17] Kim-Anh Lê Cao,et al. mixOmics: An R package for ‘omics feature selection and multiple data integration , 2017, bioRxiv.
[18] Sunil Kumar,et al. Spatial prediction of wheat septoria leaf blotch (Septoria tritici) disease severity in Central Ethiopia , 2016, Ecol. Informatics.
[19] Jose A. Jiménez-Berni,et al. Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping , 2014 .
[20] J. G. Lyon,et al. Hyperspectral Remote Sensing of Vegetation , 2011 .
[21] S. Gurr,et al. The impact of Septoria tritici Blotch disease on wheat: An EU perspective , 2015, Fungal genetics and biology : FG & B.
[22] Ruiliang Pu,et al. Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale , 2015, Precision Agriculture.
[23] Michael E. Schaepman,et al. Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .
[24] Y. Inoue,et al. Reflectance Indices Indicative of Changes in Water and Pigment Contents of Peanut and Wheat Leaves , 2004, Photosynthetica.
[25] Ethan L. Stewart,et al. An Improved Method for Measuring Quantitative Resistance to the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis. , 2016, Phytopathology.
[26] Uwe Rascher,et al. Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding , 2014 .
[27] Hao Yang,et al. Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives , 2017, Front. Plant Sci..
[28] Ben Somers,et al. Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves , 2009, Remote. Sens..
[29] Achim Walter,et al. Ranking quantitative resistance to Septoria tritici blotch in elite wheat cultivars using automated image analysis , 2017, bioRxiv.
[30] Juliane Bendig,et al. Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements , 2015 .
[31] John Spink,et al. The wheat-Septoria conflict: a new front opening up? , 2014, Trends in plant science.
[32] M. Tester,et al. Phenomics--technologies to relieve the phenotyping bottleneck. , 2011, Trends in plant science.
[33] I. Filella,et al. Reflectance assessment of mite effects on apple trees , 1995 .
[34] L. Plümer,et al. Development of spectral indices for detecting and identifying plant diseases , 2013 .
[35] Davoud Ashourloo,et al. Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina) , 2014, Remote. Sens..
[36] Ron Wehrens,et al. The pls Package: Principal Component and Partial Least Squares Regression in R , 2007 .
[37] Rong-Kuen Chen,et al. Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder , 2007 .
[38] C. Rush,et al. Comparison of Visual and Multispectral Radiometric Disease Evaluations of Cercospora Leaf Spot of Sugar Beet. , 2005, Plant disease.
[39] M. Ganal,et al. Whole Genome Association Mapping of Fusarium Head Blight Resistance in European Winter Wheat (Triticum aestivum L.) , 2013, PloS one.
[40] Georg Noga,et al. Use of blue-green and chlorophyll fluorescence measurements for differentiation between nitrogen deficiency and pathogen infection in winter wheat. , 2011, Journal of plant physiology.
[41] Erich-Christian Oerke,et al. Hyperspectral phenotyping of the reaction of grapevine genotypes to Plasmopara viticola. , 2016, Journal of experimental botany.
[42] Davoud Ashourloo,et al. Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements , 2014, Remote. Sens..
[43] Werner B. Herppich,et al. Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat , 2014 .
[44] Georg Bareth,et al. Estimate leaf chlorophyll of rice using reflectance indices and partial least squares , 2015 .
[45] Georg Bareth,et al. Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices , 2013, Remote. Sens..
[46] Lalit Kumar,et al. Imaging Spectrometry and Vegetation Science , 2001 .
[47] A. Walter,et al. Plant phenotyping: from bean weighing to image analysis , 2015, Plant Methods.
[48] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[49] B. Gao. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .
[50] N. Oppelt. Monitoring of Plant Chlorophyll and Nitrogen Status Using the Airborne Imaging Spectrometer AVIS , 2002 .
[51] Anne-Katrin Mahlein,et al. Proximal Sensing of Plant Diseases , 2014 .
[52] Hilko van der Voet,et al. Comparing the predictive accuracy of models using a simple randomization test , 1994 .
[53] Yuri A. Gritz,et al. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.