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(1990)
Determining levels
Jean. Steinier, Yves. Termonia, Jules. Deltour (1964)
Smoothing and differentiation of data by simplified least square procedure.Analytical chemistry, 44 11
R. Vlugt (2006)
Plant viruses in European Agriculture: Current problems and future aspects
M. Meroni, M. Rossini, V. Picchi, C. Panigada, S. Cogliati, C. Nali, R. Colombo (2008)
Assessing Steady-state Fluorescence and PRI from Hyperspectral Proximal Sensing as Early Indicators of Plant Stress: The Case of Ozone ExposureSensors (Basel, Switzerland), 8
A. Gitelson, M. Merzlyak, O. Chivkunova (2001)
Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves¶, 74
O. Kooten, J. Snel (1990)
The use of chlorophyll fluorescence nomenclature in plant stress physiologyPhotosynthesis Research, 25
R Vlugt (2006)
Virus diseases and crop biosecurity
I. Young, J. Walker, J. Bowie (1974)
An Analysis Technique for Biological Shape. IInf. Control., 25
A. Gitelson, G. Keydan, M. Merzlyak (2006)
Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leavesGeophysical Research Letters, 33
Chun-Chieh Yang, S. Prasher, J. Landry, H. Ramaswamy (2003)
Development of an Image Processing System and a Fuzzy Algorithm for Site-Specific Herbicide ApplicationsPrecision Agriculture, 4
Chwen-Ming Yang (2010)
Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectancePrecision Agriculture, 11
G. Heijden, J. Clevers, A. Schut (2007)
Combining close‐range and remote sensing for local assessment of biophysical characteristics of arable landInternational Journal of Remote Sensing, 28
H. Muhammed (2005)
Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in WheatBiosystems Engineering, 91
G. Polder, G. Heijden, L. Keizer, I. Young (2003)
Calibration and Characterisation of Imaging SpectrographsJournal of Near Infrared Spectroscopy, 11
A. Derks, J. Abeele, A. Schadewijk (1982)
Purification of tulip breaking virus and production of anti-sera for use in ELISANetherlands Journal of Plant Pathology, 88
L. Romanow, J. Eijk, W. Eikelboom, A. Schadewijk, D. Peters (1990)
Determining levels of resistance to Tulip Breaking Virus (TBV) in tulip (Tulipa L.) cultivarsEuphytica, 51
T. Rao (2004)
Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB
J. Clevers, L. Kooistra, M. Schaepman (2008)
Using spectral information from the NIR water absorption features for the retrieval of canopy water contentInt. J. Appl. Earth Obs. Geoinformation, 10
F. Podczeck (1997)
A shape factor to assess the shape of particles using image analysisPowder Technology, 93
B. Jähne (1997)
Practical handbook on image processing for scientific applications
D. Sims, J. Gamon (2002)
Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stagesRemote Sensing of Environment, 81
(2003)
Development of an image
(2007)
Correcting and matching time
J. Gamon, J. Surfus (1999)
Assessing leaf pigment content and activity with a reflectometerNew Phytologist, 143
W. Mowat (1985)
Tulip chlorotic blotch virus, a second potyvirus causing tulip flower breakAnnals of Applied Biology, 106
J. Gamon, Christopher Field, W. Bilger, O. Björkman, A. Fredeen, Josep Peñuelas (1990)
Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopiesOecologia, 85
G. Polder, G. Heijden, H. Jalink, J. Snel (2007)
Correcting and matching time sequence images of plant leaves using Penalized Likelihood Warping and Robust Point MatchingComputers and Electronics in Agriculture, 55
E. Dekker, A. Derks, C. Asjes, M. Lemmers, J. Bol, S. Langeveld (1993)
Characterization of potyviruses from tulip and lily which cause flower-breaking.The Journal of general virology, 74 ( Pt 5)
Y. Niimi, Dong-sheng Han, S. Mori, Hitoshi Kobayashi (2003)
Detection of cucumber mosaic virus, lily symptomless virus and lily mottle virus in Lilium species by RT-PCR techniqueScientia Horticulturae, 97
S. Samborski, N. Tremblay, E. Fallon (2009)
Strategies to Make Use of Plant Sensors-Based Diagnostic Information for Nitrogen RecommendationsAgronomy Journal, 101
C. Asjes, G. Blom-Barnhoorn (2001)
Control of aphid-vectored and thrips-borne virus spread in lily, tulip, iris and dahlia by sprays of mineral oil, polydimethylsiloxane and pyrethroid insecticide in the field.Annals of Applied Biology, 139
L. Chaerle, I. Leinonen, H. Jones, D. Straeten (2007)
Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging.Journal of experimental botany, 58 4
J. Gómez-Sanchís, L. Gómez-Chova, N. Aleixos, Gustau Camps-Valls, C. Montesinos-Herrero, E. Moltó, J. Blasco (2008)
Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarinsJournal of Food Engineering, 89
J. Beekwilder, A. Houwelingen, J.R.C.M. Beckhoven, A. Speksnijder (2008)
Stable recombinant alpaca antibodies for detection of Tulip virus XEuropean Journal of Plant Pathology, 121
(2010)
Leaf Clip
C. Asjes (1975)
Control of the spread of tulip breaking virus in tulips with mineral-oil spraysNetherlands Journal of Plant Pathology, 81
J. Jørgensen, R. Jørgensen (2007)
Uniformity of wheat yield and quality using sensor assisted application of nitrogenPrecision Agriculture, 8
G. Krause, E. Weis (1991)
Chlorophyll Fluorescence and Photosynthesis: The BasicsAnnual Review of Plant Biology, 42
The tulip breaking virus (TBV) causes severe economic losses for countries that export tulips such as the Netherlands. Infected plants have to be removed from the field as soon as possible. There is an urgent need for a rapid and objective method of screening. In this study, four proximal optical sensing techniques for the detection of TBV in tulip plants were evaluated and compared with a visual assessment by crop experts as well as with an ELISA (enzyme immunoassay) analysis of the same plants. The optical sensor techniques used were an RGB color camera, a spectrophotometer measuring from 350 to 2500 nm, a spectral imaging camera covering a spectral range from 400 to 900 nm and a chlorophyll fluorescence imaging system that measures the photosynthetic activity. Linear discriminant classification was used to compare the results of these optical techniques and the visual assessment with the ELISA score. The spectral imaging system was the best optical technique and its error was only slightly larger than the visual assessment error. The experimental results appear to be promising, and they have led to further research to develop an autonomous robot for the detection and removal of diseased tulip plants in the open field. The application of this robot system will reduce the amount of insecticides and the considerable pressure on labor for selecting diseased plants by the crop expert.
Precision Agriculture – Springer Journals
Published: Apr 23, 2010
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