Abstract
Drought stress detection involves multi-modal image analysis with high spatio-temporal resolution. Identification of digital traits that characterizes drought stress response (DSR) is challenging due to high volume of image based features. Also, the labelled data that categorizes DSR are either unavailable or subjectively developed, which is a low-throughput and error-prone task. Therefore, we propose a novel framework that provides an automated scoring of DSR based on multi-trait fusion. k-means clustering was used to extract latent drought clusters and the relevant traits were identified using Support Vector Machine-Recursive Feature Extraction (SVM-RFE). Using these traits, SVM based DSR classification model was constructed. The framework has been validated on visible and thermal shoot images of rice plants, yielding 95% accuracy. Various imaging modalities can be integrated with the proposed framework, thus making it scalable as no prior information about the DSR was assumed.
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References
Furbank, R.T., Tester, M.: Phenomics–technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16(12), 635–644 (2011)
Ghanem, M.E., Marrou, H., Sinclair, T.R.: Physiological phenotyping of plants for crop improvement. Trends Plant Sci. 20(3), 139–144 (2015)
Li, L., Zhang, Q., Huang, D.: A review of imaging techniques for plant phenotyping. Sensors 14(11), 20078–20111 (2014)
Singh, A., Ganapathysubramanian, B., Singh, A.K., Sarkar, S.: Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 21(2), 110–124 (2016)
Romer, C., Wahabzada, M., Ballvora, A., Pinto, F., Rossini, M., Panigada, C., Behmann, J., Leon, J., Thurau, C., Bauckhage, C., et al.: Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. Funct. Plant Biol. 39(11), 878–890 (2012)
Kersting, K., Xu, Z., Wahabzada, M., Bauckhage, C., Thurau, C., Roemer, C., Ballvora, A., Rascher, U., Leon, J., Pluemer, L.: Pre-symptomatic prediction of plant drought stress using Dirichlet aggregation regression on hyperspectral images. In: AAAI (2012)
Smith, H.K., Clarkson, G.J., Taylor, G., Thompson, A.J., Clarkson, J., Rajpoot, N.M., et al.: Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS ONE 9(6), e97612 (2014)
Humplík, J.F., Lazar, D., Husickova, A., Spíchal, L.: Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses–a review. Plant Methods 11(1), 1 (2015)
Chen, D., Neumann, K., Friedel, S., Kilian, B., Chen, M., Altmann, T., Klukas, C.: Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. Plant Cell 26(12), 4636–4655 (2014)
Minervini, M., Scharr, H., Tsaftaris, S.A.: Image analysis: the new bottleneck in plant phenotyping [applications corner]. IEEE Signal Process. Mag. 32(4), 126–131 (2015)
Barrs, H., Weatherley, P.: A re-examination of the relative turgidity technique for estimating water deficits in leaves. Aust. J. Biol. Sci. 15(3), 413–428 (1962)
Richardson, A.D., Duigan, S.P., Berlyn, G.P.: An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytol. 153(1), 185–194 (2002)
Munne-Bosch, S., Alegre, L.: Die and let live: leaf senescence contributes to plant survival under drought stress. Funct. Plant Biol. 31(3), 203–216 (2004)
Pask, A., Pietragalla, J.: Leaf area, green crop area and senescence. In: Pask, A., Pietragalla, J., Mullan, D., Reynolds, M. (eds.) Physiological Breeding II: A Field Guide to Wheat Phenotyping, pp. 58–62 (2012)
Neilson, E.H., Edwards, A., Blomstedt, C., Berger, B., Moller, B.L., Gleadow, R.: Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time. J. Exp. Bot. (2015). https://doi.org/10.1093/jxb/eru526
Fahlgren, N., Feldman, M., Gehan, M.A., Wilson, M.S., Shyu, C., Bryant, D.W., Hill, S.T., McEntee, C.J., Warnasooriya, S.N., Kumar, I., et al.: A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria. Mol. Plant 8(10), 1520–1535 (2015)
Boykov, O.V.Y., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1 (2001)
Klukas, C., Chen, D., Pape, J.-M.: Integrated analysis platform: an open-source information system for high-throughput plant phenotyping. Plant Physiol. 165(2), 506–518 (2014)
Breiman, L.: Statistics with a View Toward Applications, vol. 1. Houghton Mifflin Co., Boston (1973)
Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM Trans. Math. Softw. (TOMS) 22(4), 469–483 (1996)
Jones, H.G., Serraj, R., Loveys, B.R., Xiong, L., Wheaton, A., Price, A.H.: Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct. Plant Biol. 36(11), 978–989 (2009)
Leinonen, I., Jones, H.G.: Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. J. Exp. Bot. 55(401), 1423–1431 (2004)
Raghunathan, S., Stredney, D., Schmalbrock, P., Clymer, B.D.: Image registration using rigid registration and maximization of mutual information. In: The 13th Annual Medicine Meets Virtual Reality Conference, Poster Presented at: MMVR13 (2005)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, no. 14, pp. 281–297 (1967)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley, Hoboken (2009)
Hanson, A.D.: Drought resistance in rice. Nature 345, 26–27 (1990)
Maldonado, S., Weber, R.: A wrapper method for feature selection using support vector machines. Inf. Sci. 179(13), 2208–2217 (2009)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Liu, Y., You, Z., Cao, L.: A novel and quick SVM-based multi-class classifier. Pattern Recognit. 39(11), 2258–2264 (2006)
Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: NIPS, vol. 12, pp. 547–553 (1999)
Blum, A.: Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress. Field Crop. Res. 112(2), 119–123 (2009)
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Bhugra, S., Anupama, A., Chaudhury, S., Lall, B., Chugh, A. (2018). Multi-modal Image Analysis for Plant Stress Phenotyping. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_24
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