Abstract
Plant stress recognition consists of Identification, Classification, Quantification, and Prediction (ICQP) in crop stress. There are several approaches to plant stress identification. However, most of these approaches are based on the use of expert employees or invasive techniques. In general, expert employees have a good performance on different plants, but this alternative requires sufficient staff in order to guarantee quality crops. On the other hand, invasive techniques need the dismemberment of the leaves. To address this problem, an alternative is to process an image seeking to interpret patterns of the images where the plant geometry may be observed, thus removing the qualified labor dependency or the crop dismemberment, but adding the challenge of having to interpret images ambiguities correctly. Motivated by the latter, we propose a new approach for plant stress recognition using deep learning and 3D reconstruction. This strategy combines the abstraction power of deep learning and the visual patterns of plant geometry. For that, our methodology has three steps. First, the plant recognition step provides the segmentation, location, and delimitation of the crop. Second, we propose a leaf detection analysis to classify and locate the boundaries between the different leaves. Finally, we use a depth sensor and the pinhole camera model to extract a 3D reconstruction.
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References
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels. EPFL (2010)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. In: Computer Vision and Pattern Recognition, pp. 1–17 (2020). https://doi.org/10.48550/arXiv.2004.10934
Clauw, P., et al.: Leaf responses to mild drought stress in natural variants of Arabidopsis. Plant Physiol. 167(3), 800–816 (2015)
Gee, A.P., Chekhlov, D., Calway, A., Mayol-Cuevas, W.: Discovering higher level structure in visual slam. IEEE Trans. Rob. 24(5), 980–990 (2008). https://doi.org/10.1109/TRO.2008.2004641
Ghosal, S., Blystone, D., Singh, A.K., Ganapathysubramanian, B., Singh, A., Sarkar, S.: An explainable deep machine vision framework for plant stress phenotyping. Proc. Natl. Acad. Sci. 115(18), 4613–4618 (2018)
Hairmansis, A., Berger, B., Tester, M., Roy, S.J.: Image-based phenotyping for non-destructive screening of different salinity tolerance traits in rice. Rice 7(1), 1–10 (2014). https://doi.org/10.1186/s12284-014-0016-3
Khanna, R., Schmid, L., Walter, A., Nieto, J., Siegwart, R., Liebisch, F.: A spatio temporal spectral framework for plant stress phenotyping. Plant Methods 15(1), 13 (2019)
Lobos, G.A., Matus, I., Rodriguez, A., Romero-Bravo, S., Araus, J.L., del Pozo, A.: Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance. J. Integr. Plant Biol. 56(5), 470–479 (2014)
Neilson, E.H., Edwards, A.M., Blomstedt, C., Berger, B., Møller, B.L., Gleadow, R.M.: 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. 66(7), 1817–1832 (2015)
Pieruschka, R., Schurr, U., et al.: Plant phenotyping: past, present, and future. Plant Phenomics 2019, 7507131 (2019)
Singh, A.K., Ganapathysubramanian, B., Sarkar, S., Singh, A.: Deep learning for plant stress phenotyping: trends and future perspectives. Trends Plant Sci. 23(10), 883–898 (2018)
Tariq, M., et al.: Rice phenotyping. In: Sarwar, N., Atique-ur-Rehman, A.S., Hasanuzzaman, M. (eds.) Modern Techniques of Rice Crop Production, pp. 151–164. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-4955-4_11
Vakilian, K.A.: Machine learning improves our knowledge about miRNA functions towards plant abiotic stresses. Sci. Rep. 10(1), 1–10 (2020)
Vasseur, F., Bontpart, T., Dauzat, M., Granier, C., Vile, D.: Multivariate genetic analysis of plant responses to water deficit and high temperature revealed contrasting adaptive strategies. J. Exp. Bot. 65(22), 6457–6469 (2014)
Walter, A., Finger, R., Huber, R., Buchmann, N.: Opinion: smart farming is key to developing sustainable agriculture. Proc. Natl. Acad. Sci. 114(24), 6148–6150 (2017)
Zhao, J., et al.: Improved vision-based vehicle detection and classification by optimized yolov4. IEEE Access, 8590–8603 (2022). https://doi.org/10.1109/ACCESS.2022.3143365
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Ríos-Toledo, G., Pérez-Patricio, M., Cundapí-López, L.Á., Camas-Anzueto, J.L., Morales-Navarro, N.A., Osuna-Coutiño, J.A.d.J. (2023). Plant Stress Recognition Using Deep Learning and 3D Reconstruction. In: Rodríguez-González, A.Y., Pérez-Espinosa, H., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2023. Lecture Notes in Computer Science, vol 13902. Springer, Cham. https://doi.org/10.1007/978-3-031-33783-3_11
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DOI: https://doi.org/10.1007/978-3-031-33783-3_11
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