Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses
Parsons, Nicholas R., Edmondson, R. N. and Song, Yu, 1983-. (2009) Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses. Biosystems Engineering, Vol.10 (No.2). pp. 161-168. ISSN 1537-5110
WRAP_Parsons_image_analysis_ornamental_crops.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Official URL: http://dx.doi.org/10.1016/j.biosystemseng.2009.06....
Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HA Statistics
S Agriculture > SB Plant culture
|Divisions:||Faculty of Medicine > Warwick Medical School > Clinical Sciences Research Institute (CSRI)
Faculty of Medicine > Warwick Medical School > Health Sciences
Faculty of Science > Life Sciences (2010- ) > Warwick HRI (2004-2010)
Faculty of Medicine > Warwick Medical School
|Library of Congress Subject Headings (LCSH):||Greenhouse plants -- Mathematical models, Plants, Ornamental, Image analysis -- Mathematical models, Horticulture -- Mathematical models, Photography, Stereoscopic|
|Journal or Publication Title:||Biosystems Engineering|
|Official Date:||October 2009|
|Page Range:||pp. 161-168|
|Access rights to Published version:||Open Access|
|Funder:||Horticultural Development Council (Great Britain) (HDC)|
|Grant number:||PC200 (HDC), CP37 (HDC)|
Bezdek, 1981 J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York (1981).
Bishop, 1995 C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford (1995).
Brons et al., 1993 A. Brons, G. Rabatel, F. Ros, F. Sevila and C. Touzet, Plant grading by vision using neural networks and statistics, Computers and Electronics in Agriculture 9 (1) (1993), pp. 25–39.
Brosnan and Sun, 2004 T. Brosnan and D.W. Sun, Improving quality inspection of food products by computer vision – a review, Journal of Food Engineering 61 (1) (2004), pp. 3–16.
Caponetto et al., 2000 R. Caponetto, L. Fortuna, G. Nunnari, L. Occhipinti and M.G. Xibilia, Soft computing for greenhouse climate control, IEEE Transactions on Fuzzy Systems 8 (6) (2000), pp. 753–760.
Cerna and Chytry, 2005 L. Cerna and M. Chytry, Supervised classification of plant communities with artificial neural networks, Journal of Vegetation Science 16 (4) (2005), pp. 407–414.
Cheng et al., 2001 H.D. Cheng, X.H. Jiang, Y. Sun and J. Wang, Color image segmentation: advances and prospects, Pattern Recognition 34 (12) (2001), pp. 2259–2281.
DeEll et al., 1999 J. DeEll, O. van Kooten, R. Prange and D. Murr, Applications of chlorophyll fluorescence techniques in postharvest physiology, Horticultural Reviews 23 (1) (1999), pp. 69–107.
Edmondson et al., 2007 R.N. Edmondson, N. Parsons, S. Adams and Y. Song, The measurement and improvement of robust bedding plant quality and the use of digital imaging for quality assessment: so you think you know plant quality?, HDC News 139 (1) (2007), pp. 20–21.
Egmont-Petersen et al., 2002 M. Egmont-Petersen, D. de Ridder and H. Handels, Image processing with neural networks – a review, Pattern Recognition 35 (10) (2002), pp. 2279–2301.
Ehret et al., 2001 D.L. Ehret, A. Lau, S. Bittman, W. Lin and T. Shelford, Automated monitoring of greenhouse crops, Agronomie 24 (4) (2001), pp. 403–414.
Foucher et al., 2004 P. Foucher, P. Revollon, Vigouroux and B. Chassériaux, Morphological image analysis for the detection of water stress in potted forsythia, Biosystems Engineering 89 (2) (2004), pp. 131–138.
Glasbey and Horgan, 1994 C.A. Glasbey and G.W. Horgan, Image Analysis for the Biological Sciences, John Wiley & Sons, Chichester (1994).
Hardin and Hilbe, 2002 J.W. Hardin and J.M. Hilbe, Generalized Estimating Equations, Chapman and Hall, New York (2002).
Harwood and Hadley, 2004 T.D. Harwood and P. Hadley, Graphical tracking systems revisited: a practical approach to computer scheduling in horticulture, Acta Horticulturae 654 (1) (2004), pp. 179–186.
Jain, 1989 A.K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall International, New Jersey (1989).
Jayas et al., 2000 D.S. Jayas, J. Paliwal and N.S. Visen, Multi-layer neural networks for image analysis of agricultural products, Journal of Agricultural Engineering Research 77 (2) (2000), pp. 119–128. Abstract | PDF (164 K) | View Record in Scopus | Cited By in Scopus (35)
Karatzoglou et al., 2006 A. Karatzoglou, D. Meyer and K. Hornik, Support vector machines in R, Journal of Statistical Software 15 (9) (2006).
Karimi et al., 2006 Y. Karimi, S.O. Prasher, R.M. Patel and S.H. Kim, Application of support vector machine technology for weed and nitrogen stress detection in corn, Computers and Electronics in Agriculture 51 (2) (2006), pp. 99–109.
Koumpouros et al., 2004 Y. Koumpouros, B.D. Mahaman, M. Maliappis, H.C. Passam, A.B. Sideridis and V. Zorkadis, Image processing for distance diagnosis in pest management, Computers and Electronics in Agriculture 44 (2) (2004), pp. 121–131.
Langton et al., 2004 F.A. Langton, J.S. Horridge, M.D. Holdsworth and P.J.C. Hamer, Control and optimization of the greenhouse environment using infra-red sensors, Acta Horticulturae 633 (1) (2004), pp. 145–152.
MacQueen, 1967 MacQueen J B (1967). Some methods for classification and analysis of multivariate observations. In: Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297.
Pal and Pal, 1993 N.R. Pal and S.K. Pal, A review of image segmentation techniques, Pattern Recognition 26 (9) (1993), pp. 1277–1294.
Parsons et al., 2006 N.R. Parsons, R.N. Edmondson and S.G. Gilmour, A generalized estimating equation method for fitting autocorrelated ordinal score data with an application in horticultural research, Journal of the Royal Statistical Society, Series C 55 (4) (2006), pp. 507–524.
R Development Core Team, 2007 R Development Core Team 2007. R: A Language and Environment for Statistical Computing. Available from: <http://www.R-project.org>. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
Ripley, 1994 B.D. Ripley, Neural networks and related methods for classification, Journal of the Royal Statistical Society, Series B 56 (3) (1994), pp. 409–456.
Sen and Srivastava, 1997 A. Sen and M. Srivastava, Regression Analysis: Theory, Methods and Applications, Springer-Verlag, New York (1997).
Song, 2008 Song Y (2008). Modelling and analysis of plant image data for crop growth and monitoring in horticulture. Unpublished PhD thesis. University of Warwick, UK.
Song et al., 2007 Song Y; Wilson R G; Edmondson R N; Parsons N R (2007). Surface modelling of plants from stereo images. In: Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007), pp. 312–319.
Stone, 1974 M. Stone, Cross-validatory choice and assessment of statistical predictions, Journal of the Royal Statistical Society, Series B 36 (2) (1974), pp. 111–147. MathSciNet
Takizawa et al., 2005 Takizawa H; Ezaki N; Mizuno S; Yamamoto S (2005). Measurement of plants by stereo vision for agricultural applications. In: Proceedings of the Seventh IASTED International Conference, Signal and Image Processing, Hawaii. p. 238.
The MathWorks, 2007 The MathWorks (2007). Matlab. Available from: <http://www.mathworks.com>.
Timmermans and Hulzebosch, 1996 A.J.M. Timmermans and A.A. Hulzebosch, Computer vision system for on-line sorting of pot plants using an artificial neural network classifier, Computers and Electronics in Agriculture 15 (1) (1996), pp. 41–55.
Titterington et al., 1981 D.M. Titterington, G.D. Murray, L.S. Murray, D.J. Spiegelhalter, A.M. Skene, J.D.F. Habbema and G.J. Gelpke, Comparison of discrimination techniques applied to a complex data set of head injured patients, Journal of the Royal Statistical Society, Series A 144 (2) (1981), pp. 145–175.
Van Kaam, 2001 C.J.H.M. Van Kaam, Neural networks used for classification of potted plants, Acta Horticulturae 562 (1) (2001), pp. 109–115.
Van Kooten et al., 1991 O. Van Kooten, M. Mensink, E. Otma and W. van Doorn, Determination of the physiological state of potted plants and cut flowers by modulated chlorophyll fluorescence, Acta Horticulturae 298 (1) (1991), pp. 83–91.
Vapnik, 2000 V.N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York (2000).
Woodford, 2008 B.J. Woodford, Evolving neurocomputing systems for horticulture applications, Applied Soft Computing 8 (1) (2008), pp. 564–578.
Zwiggelaar, 1998 R. Zwiggelaar, A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops, Crop Protection 17 (3) (1998), pp. 189–206.
Actions (login required)