Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses

Biosystems Engineering

Volumn 104, Issue 2, 2009, Pages 161-168

  Parsons, N R ^a   Edmondson, R N ^a   Song, Y ^a,b  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CROPS; FEEDFORWARD NEURAL NETWORKS; GLASS; GRADING; IMAGE ANALYSIS; IMAGE SEGMENTATION; QUALITY CONTROL; STEREO IMAGE PROCESSING;

FEED-FORWARD ARTIFICIAL NEURAL NETWORKS; MONITORING AND ASSESSMENT; OBJECTIVE MEASUREMENT; ORNAMENTAL CROPS; ORNAMENTAL HORTICULTURE; QUALITY ASSESSMENT; SEGMENTED IMAGES; STATISTICAL MODELLING;

IMAGE QUALITY;

EID: 69749122512     PISSN: 15375110     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystemseng.2009.06.015     Document Type: Article

References (40)

1. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
2. Bishop C.M. Neural Networks for Pattern Recognition (1995), Oxford University Press, Oxford
3. Brons A., Rabatel G., Ros F., Sevila F., and Touzet C. Plant grading by vision using neural networks and statistics. Computers and Electronics in Agriculture 9 1 (1993) 25-39
4. Brosnan T., and Sun D.W. Improving quality inspection of food products by computer vision - a review. Journal of Food Engineering 61 1 (2004) 3-16
5. Caponetto R., Fortuna L., Nunnari G., Occhipinti L., and Xibilia M.G. Soft computing for greenhouse climate control. IEEE Transactions on Fuzzy Systems 8 6 (2000) 753-760
6. Cerna L., and Chytry M. Supervised classification of plant communities with artificial neural networks. Journal of Vegetation Science 16 4 (2005) 407-414
7. Cheng H.D., Jiang X.H., Sun Y., and Wang J. Color image segmentation: advances and prospects. Pattern Recognition 34 12 (2001) 2259-2281
8. DeEll J., van Kooten O., Prange R., and Murr D. Applications of chlorophyll fluorescence techniques in postharvest physiology. Horticultural Reviews 23 1 (1999) 69-107
9. Edmondson R.N., Parsons N., Adams S., and Song Y. 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) 20-21
10. Egmont-Petersen M., de Ridder D., and Handels H. Image processing with neural networks - a review. Pattern Recognition 35 10 (2002) 2279-2301
11. Ehret D.L., Lau A., Bittman S., Lin W., and Shelford T. Automated monitoring of greenhouse crops. Agronomie 24 4 (2001) 403-414
12. Foucher P., Revollon P., Vigouroux, and Chassériaux B. Morphological image analysis for the detection of water stress in potted forsythia. Biosystems Engineering 89 2 (2004) 131-138
13. Glasbey C.A., and Horgan G.W. Image Analysis for the Biological Sciences (1994), John Wiley & Sons, Chichester
14. Hardin J.W., and Hilbe J.M. Generalized Estimating Equations (2002), Chapman and Hall, New York
15. Harwood T.D., and Hadley P. Graphical tracking systems revisited: a practical approach to computer scheduling in horticulture. Acta Horticulturae 654 1 (2004) 179-186
16. Jain A.K. Fundamentals of Digital Image Processing (1989), Prentice-Hall International, New Jersey
17. Jayas D.S., Paliwal J., and Visen N.S. Multi-layer neural networks for image analysis of agricultural products. Journal of Agricultural Engineering Research 77 2 (2000) 119-128
18. Karatzoglou A., Meyer D., and Hornik K. Support vector machines in R. Journal of Statistical Software 15 9 (2006)
19. Karimi Y., Prasher S.O., Patel R.M., and Kim S.H. Application of support vector machine technology for weed and nitrogen stress detection in corn. Computers and Electronics in Agriculture 51 2 (2006) 99-109
20. Koumpouros Y., Mahaman B.D., Maliappis M., Passam H.C., Sideridis A.B., and Zorkadis V. Image processing for distance diagnosis in pest management. Computers and Electronics in Agriculture 44 2 (2004) 121-131
21. Langton F.A., Horridge J.S., Holdsworth M.D., and Hamer P.J.C. Control and optimization of the greenhouse environment using infra-red sensors. Acta Horticulturae 633 1 (2004) 145-152
22. 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.
23. Pal N.R., and Pal S.K. A review of image segmentation techniques. Pattern Recognition 26 9 (1993) 1277-1294
24. Parsons N R (2004). Statistical methods for improving pot-plant quality and robustness. Unpublished PhD thesis. University of London, UK.
25. Parsons N.R., Edmondson R.N., and Gilmour S.G. 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) 507-524
26. R Development Core Team 2007. R: A Language and Environment for Statistical Computing. Available from: . R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
27. Ripley B.D. Neural networks and related methods for classification. Journal of the Royal Statistical Society, Series B 56 3 (1994) 409-456
28. Sen A., and Srivastava M. Regression Analysis: Theory, Methods and Applications (1997), Springer-Verlag, New York
29. Song Y (2008). Modelling and analysis of plant image data for crop growth and monitoring in horticulture. Unpublished PhD thesis. University of Warwick, UK.
30. 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.
31. Stone M. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B 36 2 (1974) 111-147
32. 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.
33. The MathWorks (2007). Matlab. Available from: .
34. Timmermans A.J.M., and Hulzebosch A.A. Computer vision system for on-line sorting of pot plants using an artificial neural network classifier. Computers and Electronics in Agriculture 15 1 (1996) 41-55
35. Titterington D.M., Murray G.D., Murray L.S., Spiegelhalter D.J., Skene A.M., Habbema J.D.F., and Gelpke G.J. 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) 145-175
36. Van Kaam C.J.H.M. Neural networks used for classification of potted plants. Acta Horticulturae 562 1 (2001) 109-115
37. Van Kooten O., Mensink M., Otma E., and van Doorn W. Determination of the physiological state of potted plants and cut flowers by modulated chlorophyll fluorescence. Acta Horticulturae 298 1 (1991) 83-91
38. Vapnik V.N. The Nature of Statistical Learning Theory (2000), Springer, New York
39. Woodford B.J. Evolving neurocomputing systems for horticulture applications. Applied Soft Computing 8 1 (2008) 564-578
40. Zwiggelaar R. A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. Crop Protection 17 3 (1998) 189-206