Determination of the most suitable color space for intelligent water stress discrimination for plants inside the greenhouse (Case Study: Coleus)

Document Type : Research Paper


University of Khuzestan, Ahvaz, Khuzestan


A precise estimation of required water for plant depends on many factors, among which the percentage of ground cover is a key parameter. Digital image processing and machine vision can be widely used to obtain this parameter in irrigation management applications. The aim of this study was to recognize the required water for plants based on color parameters of plants’ ground cover; therefore, different color spaces (RGB, rgb, XYZ, HSV, HLS, L*a*b, L*u*v*, YCbCr, YUV, TSL and I1I2I3) were applied on the set of ornamental shrub images with the scientific name of Plectranthusscutellarioides in two positions (fresh and wilting).Each color space demonstrated different probability distribution ofa given color class corresponded to two plant conditions (fresh and wilting). Thus, after examining the color spaces, both statistically and visually, the suitable color spaces were selected. Finally, histograms of suitable color spaces have been used to train the Bayesian Classifier. The Bayesian classifier detected two conditions of plant (fresh and wilting) with precision 83.11%. In general, on the basis of information obtained from images histograms, (frequency of pixels’ intensity) plantswater status for irrigationschedulingwas recognizable.


Main Subjects

Allen, R. G. & Pereira, L. S. (2009). Estimating crop coefficients from fraction of groundcover and height. Irrig. Sci. 28, 17–34.
Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. (1998). Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56. FAO, Rome. 6541 p.
Arefi, A., Motlagh, A. M., Mollazade, K. & Teimourlou, R. F. (2011). Recognition and localization of ripen tomato based on machine vision. Australian Journal of Crop Science. 5(10),1144-1149.
Astrand, B. & Baerveldt, A. J. (2002). An agricultural mobile robot with vision-based perception for mechanical weed control. Autonomous Robots. 13, 21–35.
Blasco, J., Aleixos, N., Roger, J. M., Rabatel, G. & Molto, E. (2002). Robotic weed control using machine vision. Biosyst. Eng. 83(2), 149–157.
Campos, I., Neale, C. M., Calera, A., Balbontin, C. & Gonzalez-Piqueras, J. (2010). Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L). Agric. Water Manage. 98, 45–54.
Chou, J. J., Chen, C. P. & Yeh, J. T. (2007). Crop identification with wavelet packet analysis and weighted Bayesian distance. Computers and electronics in agriculture, 57(1), 88-98.
Damas, M., Prados, A .M., Gomez, F. & Olivares, G. (2001). Hidro Bus system: fieldbus for integrated management of extensive areas of irrigated land. Microprocessors and Microsystems, 25(3), 177-184.
Dorigo, M. & Stutzle, T. (2004). Ant colony optimization. A Bradford book. The United States of America: The MIT Press. p. 321.
Escarabajal-Henarejos, D., Molina-Martinez, J. M., Fernandez-Pacheco, D. G. & Garcia-Mateos, G. (2015). Methodology for obtaining prediction models of root depth of lettuce for its application in irrigation automation. Agric. Water Manage. 151, 167–173.
Fernandez-Pacheco, D. G., Escarabajal, D., Ruiz-Canales, A., Conesa, J. & Molina-Martinez, J. M. (2014). A digital image-processing-based method for determining the crop coefficient of lettuce crops in the southeast of Spain. Biosyst. Eng. 117, 23–34.
Foucher, P., Revollon, P., Vigouroux, B. & Chasseriaux, G. (2004). Morphological image analysis for the detection of water stress in potted Forsythia. Biosystems Engineering, 89 (2), 131-138.
Garcia-Mateos, G., Hernandez-Hernandez, J. L., Escarabajal-Henarejos, D., Jaen-Terrones, S. & Molina-Martinez, J. M. (2015). Study and comparison of color models for automatic image analysis in irrigation management applications. Agricultural Water Management. 151, 158-66.
Giacomelli, G. A., Ling, P. P. & Kole, J. (1998). Determining nutrient stress in lettuce plants with machine vision technology. Hort Technology. 8(3), 361–365.
Golzarian1, M. R., Sadeghi, F., Ghanei, N. & Kazemi, F. (2014). A qualitative and quantitative approach to assessing the performance of contrast enhancing colour indices used in automatic computer vision plant identification system. Conference: The 8th National Congress on Agr. Machinery (Biosystem) Engineering and Mechanization., At Mashad, Iran. pp. 1579-1592. (in Farsi).
Gonzalez, R. C., Woods, R. E. & Eddins, S. L. (2004). Digital image processing using MATLAB. Pearson Education India.
Grant, O. M., Davies, M. J., Longbottom, H. & Harrison-Murray, R. (2012). Evapotranspiration of container ornamental shrubs: modelling crop-specific factors for a diverse range of crops. Irrigation Science. 30(1), 1–12.
Hanson, B. R. & May, D. M. (2005). Crop coefficients for drip-irrigated processing tomato. Agric. Water Manage. 81(3), 381–399.
Hendrawan, Y. & Murase, M. (2011). Bio inspired feature selection to select informative image features for determining water content of cultured Sunagoke moss. Expert Systems with Applications, 38(11), 14321–14335.
HunterLab. (2001). Application note. Insight on Color. 13, pp. 1-4.
Kakumanu, P., Makrogiannis, S. & Bourbakis, N. (2007). A survey of skin-color modeling and detection methods. Pattern Recognit. V. 40(3), 1106–1122.
Kodagali, J. A. & Balaji, S. (2012). Computer vision and image analysis based techniques for automatic characterization of fruits–a review. Int. J. Comput. Appl. 50(6),6–12.
Kumar, P., Sengupta, K. & Lee, A. (2002). A comparative study of different color spaces for foreground and shadow detection for traffic monitoring system. The IEEE 5thInternational Conference on Intelligent Transportation Systems. 100–105.
Leemans, V. & Destain, M. F. (2004). A real-time grading method of apples based on features extracted from defects. Journal of Food Engineering. 61(1), 83-89.
Lin, K., Chen, J., Si, H. & Junhui, W. (2013). A review on computer vision technologies applied in greenhouse plant stress detection. Adv. Image Graphics Technol. 363, 192–200.
Ling, P. P. & Ruzhitsky, V. N. (1996). Machine vision techniques for measuring the canopy of tomato seedling. J. Agric. Eng. Res. 65, 85–95.
Lopez-Urrea, R., Martin de Santa Olalla, F., Montoro, A. & Lopez-Fuster, P. (2009). Single and dual crop coefficients and water requirements for onion (Allium cepa L.) under semiarid conditions. Agric. Water Manage. 96, 1031–1036.
Luzuriaga, D. A. & Balaban, M. O. (2002). Colour machine vision system: an alternative for colour measurement. In Proceedings of the world congress of computers in agriculture and natural resources, Iguacu Falls, Brazil. 13–15 March. pp. 93–100.
McCarthy, C. L., Cheryl, N. H. & Hancock, S. R. (2010). Applied machine vision of plants-a review with implications for field deployment in automated farming operations. Intell. Serv. Rob. 3(4), 209–217.
Ohta, Y., Kanade, T. & Sakai, T. (1980). Color information for region segmentation. Comput. Graphics Image Process. 13(3), 222–241.
Shahin, M. A., Tollner, E. W., Gitaitis, R. D., Sumner, D. R. & Maw, B. W. (2002). Classification of sweet onions based on internal defects using image processing and neural network techniques. Transactions of the ASAE. 45(5), 1613–1618.
Shih, P. & Liu, C. (2005). Comparative assessment of content-based face image retrieval in different color spaces. Int. J. Patt. Recogn. Artif. Intell. 19(7), 873–893.
Shiraishi, M. & Sumiya, H. (1996). Plant identification from leaves using quasi-sensor fusion. J. Manuf. Sci. Eng., Trans. ASME. 118(3), 382–387.
Shoor, M., Behzadi, M. & Goldani, M. (2012). Study of Rooting, Quantitative and Anatomical Traits on Two Coleus Spices in High Level Carbon Dioxide. Journal of Horticultural Science, 26(3), 277-285.
Slaughter, D. C., Giles, D. K. & Downey, D. (2008). Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61, 63–78.
Steward, B. L., Tian, L. F., Nettleton, D. & Tang, L. (2004). Reduced-dimension clustering for vegetation segmentation. Trans. ASAE. 47(2), 609–616.
Story, D., Kacira, M., Kubota, C., Akoglu, A. & An, L. L. (2010). Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Comput. Electron. Agric. 74(2), 238–243.
Tellaeche, A., Burgos-Artizzu, X. P., Pajares, G. & Ribeiro, A. (2008). A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognition, 41(2), 521-530.
Terrillon, J. C. & Akamatsu, S. (2000). Comparative performance of different chrominance spaces for color segmentation and detection of human faces in complex scene images. In: International Conf. on Face and Gesture Recognition, pp. 54–61.
Weizheng, S., Yachun, W., Zhanliang, C. & Hongda, W. (2008). Grading method of leaf spot disease based on image processing. In Computer Science and Software Engineering, International Conference. 6, 491-494.
Xu, X. G., Wang, J. H., Li, C. J., Song, X. U. & Huang, W. J. (2010). Estimating growth height of winter wheat with remote sensing. In: In Proceedings of the SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII.
Zhao, Y., Gong, L., Zhou, B., Huang, Y. & Liu, C. (2016). Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis. Biosystems Engineering. 148, 127-137.