Method for Segmenting Tomato Plants in Uncontrolled Environments

Deny Lizbeth Hernández-Rabadán, Julian Guerrero, Fernando Ramos-Quintana

Tecnológico de Monterrey Campus Cuernavaca, Morelos, México.

DOI: 10.4236/eng.2012.410076                

Abstract

Segmenting vegetation in color images is a complex task, especially when the background and lighting conditions of the environment are uncontrolled. This paper proposes a vegetation segmentation algorithm that combines a supervised and an unsupervised learning method to segment healthy and diseased plant images from the background. During the training stage, a Self-Organizing Map (SOM) neural network is applied to create different color groups from a set of images containing vegetation, acquired from a tomato greenhouse. The color groups are labeled as vegetation and non-vegetation and then used to create two color histogram models corresponding to vegetation and non-vegetation. In the online mode, input images are segmented by a Bayesian classifier using the two histogram models. This algorithm has provided a qualitatively better segmentation rate of images containing plants’ foliage in uncontrolled environments than the segmentation rate obtained by a color index technique, resulting in the elimination of the background and the preservation of important color information. This segmentation method will be applied in disease diagnosis of tomato plants in greenhouses as future work.

Keywords

Image Segmentation; Color Images; Self-Organizing Maps; Bayesian Classifier

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. Hemming, and T. Rath , “Computer-Vision-based Weed Identification under Field Conditions Using Controlled Lighting,” Journal of Agricultural and Engineering Research, Vol. 78, No.3, 2001, pp. 233-243. doi:10.1006/jaer.2000.0639
[2]	J. Ronghua, F. Zetian and Q. Lijun. “Real-Time Plant Image Segmentation Algorithm Under Natural Outdoor Light Conditions,” New Zealand Journal Of Agricultural Research, Vol.50, No.5, 2007, pp. 847-854. doi:10.1080/00288230709510359
[3]	W. Hongxia and L. Mingxi, “A Method of Tomate Image Segmentation Based on Mutual Information and Threshold Iteration,” in IFIP International Federation for Information Proccesing, Vol. 294, Computers and Computing Technologies in Agriculture II, Vol. 2, Eds. D. Li, Z. Chunjiang, Springer Boston, 2009, pp. 1097-1104. doi: 10.1007/978-1-4419-0211-5_36
[4]	A. J. Perez, F. López, J. V. Benlloch and S. Christensen, “Colour and Shape Analysis Techniques for Weed Detection in Cereal Fields,” Computers and Electronics in Agriculture, Vol. 25, No. 3, 2000, pp. 197-212. doi:10.1016/S0168-1699(99)00068-X
[5]	T. Kataoka, T. Kaneko, H. Okamoto and S. Hata, “Crop Growth Estimation System Using Machine Vision,” Proceeding of the 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Vol. 2, 20-24 July 2003, pp 1079-1083. doi:10.1109/AIM.2003.1225492
[6]	J. C. Neto, G. E. Meyer and D. D. Jones, “Individual Leaf Extractions From Young Canopy Images Using Gustafson-Kessel Clustering and a Genetic Algorithm,” Computers and Electronics in Agriculture, Vol. 51, No. 1-2, 2006, pp. 66-85. doi:10.1016/j.compag.2005.11.002
[7]	G. E. Meyer and J. C. Neto, “Verification of Color Vegetation Indices for Automated Crop Imaging Applications”, Computers and Electronics in Agriculture, Vol. 63, No. 2, 2008, pp. 282-293. doi:10.1016/j.compag.2008.03.009
[8]	L. Zheng, J. Zhang and Q. Wang, “Mean-Shift-based Color Segmentation of Images Containing Green Vegetation”, Computers and Electronics in Agriculture, Vol. 65, No.1, 2009, pp. 93-98.doi:10.1016/j.compag.2008.08.002
[9]	A. Meunkaew-jinda, P. Kumsawat, K. Attakitmongcol and A. Srikaew, “Grape Leaf Disease Detection from Color Imagery Using Hybrid Intelligent System”, 5th. International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Vol.1, Krabi, 14-17 May 2008, pp. 513-516. doi:10.1109/ECTICON.2008.4600483
[10]	T. Kohonen, “Self-Organized Formation of Topologically Correct Feature Maps”, Biological Cybernetics, Vol. 43, No.1, 1982, pp. 59-69. doi:10.1007/BF00337288
[11]	N. Li and Y. F. Li, “Feature Encoding for Unsupervised Segmentation of Color Images,” IEEE Transactions on Systems Man and Cybernetics, Part B: Cybernetics, Vol.33, No.3, 2003, pp. 438-447. doi:10.1109/TSMCB.2003.811120
[12]	S. Dongcheng, X. Yashu and Z. Long, “Moving Object Detection Based on Scene Understanding,” International Conference on Information Engineering and Computer Science, Wuhan, 19-20 December 2009, pp.1-4. doi:10.1109/ICIECS.2009.5365256
[13]	Y. Kun, Z. Hong and P. Ying-jie, “Human Face Detection Based on SOFM Neural Network,” IEEE International Conference on Information Acquisition, Weihai, 20-23 August 2006, pp. 1253-1257. doi:10.1109/ICIA.2006.305929
[14]	Y. Wu, Q. Liu and T. S. Huang, “An Adaptive Self-Organizing Color Segmentation Algorithm with Application to Robust Real-time Human Hand Localization,” Proceedings of the Fourth Asian Conference on Computer Vision, Taipei, January 2000, pp.1106-1111.
[15]	B. Doungchatom, P. Kumsawat, K. Attakitmongcol and A. Srikeaw, “Modified Self-Organizing Map for Optical Flow Clustering System,” Proceedings of the 7th WSEAS International Conference on Signal, Speech and Image Processing, Beijing, 15-17 September 2007, pp.61-69.
[16]	Z. Zhou, S. Wei, X. Zhang and X. Zhao, “Remote Sensing Image Segmentation Based on Self-Organizing Map at Multiple-Scale,” Proceedings of SPIE Geoinformatics: Remotely Sensed Data and Information, Vol. 6752, 25 May 2007, Nanjing, pp. 122-126. doi:10.1117/12.760420
[17]	M. Awad, K. Chehdi and A. Nasri, “Multicomponent Image Segmentation Using Genetic Algorithm and Artificial Neural Network,” IEEE Geosciences and Remote Sensing Letters, Vol. 4, No. 4, 2007, pp. 571-575. doi:10.1109/LGRS.2007.903064
[18]	M. Awad, K. Chehdi and A. Nasri, “Multi-component Image Segmentation Using a Hybrid Dynamic Genetic Algorithm and Fuzzy C-Means,” IET image processing, Vol. 3, No. 2, 2009, pp. 52-62. doi:10.1049/iet-ipr.2007.0213
[19]	H. Yin, “The Self-Organizing Maps: Background, Theories, Extensions and Applications,” Studies in Computational Intelligence (SCI), Vol.115, 2008, pp. 715-762. doi:10.1007/978-3-540-78293-3_17
[20]	L. F. Tian and D. C. Slaughter, “Environmentally Adaptive Segmentation Algorithm for Outdoor Image Segmentation,” Computers and Electronics in Agriculture, Vol. 21, No.3, 1998, pp.153-168. doi:10.1016/S0168-1699(98)00037-4
[21]	U. Watchareeruetai, Y. Takeuchi, T. Matsumoto, H. Kudo and N. Ohnishi, “Computer Vision Based Methods for Detecting Weeds in Lawns,” IEEE Conference on Cybernetics and Intelligent Systems, Bangkok, 7-9 June 2006, pp. 1-6. doi:10.1109/ICCIS.2006.252275
[22]	A. Tellaeche, X. P. Burgos-Artizzu, G. Pajares and A. Ribeiro, “A Vision-based Method for Weeds Identification Trough the Bayesian Decision Theory,” The Journal of the Pattern Recognition Society, Vol. 41, No. 2, 2008, pp. 521-530. doi:10.1016/j.patcog.2007.07.007
[23]	T. Mitchell, “Machine Learning,” McGraw Hill Inc., New York, 1997.
[24]	Q. Huynh-Thu, M. Meguro and M. Kaneko, “Skin-Color Extraction in Images with Complex Background and Varying Illumination,” Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision, 2002, pp. 280-285. doi:10.1109/ACV.2002.1182195