Rice yellow mottle is considered the most destructive disease threatening rice production in Africa. Early detection of this infection in rice is essential to limit its expansion and proliferation. However, there is no research devoted to the spectral detection of rice yellow mottle virus (RYMV) infection, especially in the asymptomatic or early stages. This work proposes the use of hyperspectral fluorescence and reflectance data at leaf level for the detection of this disease in asymptomatic stages. A greenhouse experiment was therefore conducted to collect hyperspectral fluorescence and reflectance data at different stages of infection. These data allowed to calculate nine vegetation indices: one from fluorescence spectra and eight from reflectance spectra. A t-test made it possible to identify, from the second day after infection, four relevant reflectance vegetation indices to discriminate healthy leaves from those infected: these are Photochemical Reflectance Index (PRI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Structure Intensive Pigment Index (SIPI) and Simple Ratio Pigment Index (SRPI). The fluorescence index was less sensitive in detecting infection. The four significant vegetation indices for the detection of RYMV were then used to build and evaluate models for discriminating plants according to their health status by the supervised classification of support vector machine (SVM) at different stages of infection. The maximum overall accuracy is 92.5% six days after inoculation (6 DAI). The sixth day after inoculation would be the adequate day to detect RYMV. This plants discrimination was validated by the mean reflectance spectra and by the histograms showing the differences between the average reflectance vegetation indices values of the two types of plants. Our results demonstrate the feasibility of differentiating RYMV-infected samples. They suggest that support vector machine learning models could be developed to diagnose RYMV-infected plants based on vegetation indices derived from spectral profiles at early stages of disease development.
References
[1]
The French Agricultural Research Centre for International Development (2022) Synthèse de la feuille de route riz: Vers une riziculture durable [2023-2033]. https://www.cirad.fr/view_pdf/7638
[2]
Tano, J.S.C. and Koné, A. (2021) Analyse des effets de distorsions sur la production du riz paddy en Côte d’Ivoire. Revue Marocaine des Sciences Agronomiques et Vétérinaires, 9, 307-316.
[3]
Bakker, W. (1975) Rice Yellow Mottle Virus. In: Harrison, B.D. and Murant, A.F., Eds., CMI/AAB Description of Plant Viruses (Vol. 149), Commonwealth Mycological Institute/Association of Applied Biologist, Wellesbourne, 1-4.
[4]
Abo, M.E. (1998) The Mode of Transmission of Rice Yellow Mottle Sobemovirus. Ph.D. Thesis, Ahmadu Bello University, Zaria.
[5]
Abo, M.E., Alegbejo, M.D., Sy, A.A. and Misari, S.M. (2000) Overview of the Mode of Transmission, Host Plants and Methods of Detection of Rice yellow mottle virus. Journal of Sustainable Agriculture, 17, 19-35. https://doi.org/10.1300/J064v17n01_04
[6]
Banwo, O.O., Makundi, R.H., Abdallah, R.S. and Mbapilla, J.C. (2001) Identification of Vectors of Rice yellow mottle virus in Tanzania. Archives of Phytopathology and Plant Protection, 33, 395-403. https://doi.org/10.1080/03235400109383361
[7]
Sarra, S., Oevering, P., Guindo, S. and Peters, D. (2004) Wind-Mediated Spread of Rice yellow mottle virus (RYMV) in Irrigated Rice Crops. Plant Pathology, 53, 148-153. https://doi.org/10.1111/j.0032-0862.2004.00981.x
[8]
Lett, J.M. (1997) Approche histopathologique de la résistance du riz (Oryza sativa) au virus de la panachure jaune du riz (RYMV). Rapport de DEA. Université Paris VI/Université Paris XI.
[9]
Thiémélé Deless, E.F., Kouassi, K.N., Issali, A.E., Albar, L., Aké, S. and Ghesquière, A. (2015) Lutte contre la panachure jaune du riz: Identification de sources de résistance chez le riz africain O. Glaberrima. Fiche technique, WAAPP/PPAAO/CNRA. http://www.waapp-ppaao.org/sites/default/files/lutte_contre_la_panachure_jaune_du_riz_identification_de_sources_de_resistance_chez_le_riz_africain_1.pdf
[10]
Raymundo, S.A. and Buddenhagen, I.W. (1976) A Rice Disease in West Africa. International Rice Commission Newsletter, 25, 58.
[11]
Awoderu, V.A. (1991) The Rice yellow mottle virus Situation in West Africa. Journal of Basic Microbiology, 31, 91-99. https://doi.org/10.1002/jobm.3620310204
[12]
N’Guessan, P., Pinel, A., Sy, A., Ghesquière, A. and Fargette, D. (2001) Distribution, Pathogenicity and Interactions of Two Strains of Rice yellow mottle virus in Forested and Savanna Zones of West Africa. Plant Disease, 85, 59-64. https://doi.org/10.1094/PDIS.2001.85.1.59
[13]
Kashyap, B. and Kumar, R. (2021) Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests. Inventions, 6, Article 29. https://doi.org/10.3390/inventions6020029
[14]
Fernández, C.I., Leblon, B., Haddadi, A., Wang, K. and Wang, J. (2020) Potato Late Blight Detection at the Leaf and Canopy Levels Based in the Red and Red-Edge Spectral Regions. Remote Sensing, 12, Article 1292. https://doi.org/10.3390/rs12081292
[15]
Moshou, D., Pantazi, X.E., Kateris, D. and Gravalos, I. (2014) Water Stress Detection Based on Optical Multisensor Fusion with a Least Squares Support Vector Machine Classifier. Biosystems Engineering, 117, 15-22. https://doi.org/10.1016/j.biosystemseng.2013.07.008
[16]
Herrmann, I., Vosberg, S.K., Ravindran, P., Singh, A., Chang, H.X., Chilvers, M.I. and Townsend, P.A. (2018) Leaf and Canopy Level Detection of Fusarium Virguliforme (sudden Death Syndrome) in Soybean. Remote Sensing, 10, Article 426. https://doi.org/10.3390/rs10030426
[17]
Tian, L., Xue, B., Wang, Z., Li, D., Yao, X., Cao, Q. and Cheng, T. (2021) Spectroscopic Detection of Rice Leaf Blast Infection from Asymptomatic to Mild Stages with Integrated Machine Learning and Feature Selection. Remote Sensing of Environment, 257, Article ID: 112350. https://doi.org/10.1016/j.rse.2021.112350
[18]
Traoré, V.S.E., Néya, B.J., Camara, M., Gracen, V., Offei, S.K. and Traoré, O. (2015) Perception and Impact of Rice yellow mottle disease on Rice Yields in Burkina Faso. Agricultural Sciences, 6, 943-952. https://doi.org/10.4236/as.2015.69091
[19]
Guinagui, N., Sorho, F., Souleymane, S., Koné, B. and Koné, D. (2019) Effect of Rice yellow mottle virus, Sobemovirus on the Contents of N P K Ca and Mg in Leaves of Infected Rice. Annual Research & Review in Biology, 30, 1-10. https://doi.org/10.9734/ARRB/2018/46625
[20]
Soro, A.P., Zoro-Diama, E.G., Diomandé, K.S., Bany, G.E., Bisseyou, Y.B.M. and Adohi-Krou, A.V. (2016) Characterization of Water and Nitrogen Stress of Maize by Laser Induced Fluorescence. Applied Physics Research, 8, 64-72. https://doi.org/10.5539/apr.v8n4p64
[21]
Zoro-Diama, E.G., Soro, A.P., Diomandé, K.S., Dian, K., Kamate, A. and Adohi-Krou, A.V. (2017) Water Deficiency Detection of Hevea brasiliensis Clones by Laser Induced Fluorescence. Applied Physics Research, 9, 36-41. https://doi.org/10.5539/apr.v9n5p36
[22]
Methy, M., Lacaze, B. and Olioso, A. (1991) Prospects and Limits of Fluorescence for Remote Sensing of the Water Status of a Plant Cover: Case of a Soybean Crop. International Journal of Remote Sensing, 12, 223-230. https://doi.org/10.1080/01431169108929648
[23]
Dobrowski, S.Z., Pushnik, J.C., Zarco-Tejada, P.J. and Ustin, S.L. (2005) Simple Reflectance Indices Track Heat and Water Stress-Induced Changes in Steady-State Chlorophyll Fluorescence at the Canopy Scale. Remote Sensing of Environment, 97, 403-414. https://doi.org/10.1016/j.rse.2005.05.006
[24]
Sun, P., Grignetti, A., Liu, S., Casacchia, R., Salvatori, R., Pietrini, F., Loreto, F. and Centritto, M. (2008) Associated Changes in Physiological Parameters and Spectral Reflectance Indices in Olive (Olea europaea L.) Leaves in Response to Different Levels of Water Stress. International Journal of Remote Sensing, 29, 1725-1743. https://doi.org/10.1080/01431160701373754
[25]
Gamon, J.A., Penuelas, J. and Field, C.B. (1992) A Narrow-Waveband Spectral Index That Tracks Diurnal Changes in Photosynthetic Efficiency. Remote Sensing of Environment, 41, 35-44. https://doi.org/10.1016/0034-4257(92)90059-S
[26]
Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W. (1974) Monitoring Vegetation Systems in the Great Plains with ERTS. NASA Special Publication, 351, 309-317.
[27]
Peñuelas, J., Filella, I. and Gamon, J.A. (1995) Assessment of Photosynthetic Radiation-Use Efficiency with Spectral Reflectance. New Phytologist, 131, 291-296. https://doi.org/10.1111/j.1469-8137.1995.tb03064.x
[28]
Peñuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J. and Field, C.B. (1994) Reflectance Indices Associated with Physiological Changes in Nitrogen-and Water-Limited Sunflower Leaves. Remote Sensing of Environment, 48, 135-146. https://doi.org/10.1016/0034-4257(94)90136-8
[29]
Guyot, G., Baret, F. and Major, D.J. (1988) High Spectral Resolution: Determination of Spectral Shifts between the Red and Infrared. International Archives of Photogrammetry and Remote Sensing, 11, 750-760.
[30]
Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J. and Dextraze, L. (2002) Integrated Narrow-Band Vegetation Indices for Prediction of Crop Chlorophyll Content for Application to Precision Agriculture. Remote Sensing of Environment, 81, 416-426. https://doi.org/10.1016/S0034-4257(02)00018-4
[31]
Savitzky, A. and Golay, M.J. (1964) Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36, 1627-1639. https://doi.org/10.1021/ac60214a047
[32]
Isaksson, T. and Næs, T. (1988) The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy. Applied Spectroscopy, 42, 1273-1284. https://doi.org/10.1366/0003702884429869
[33]
Cortes, C. and Vapnik, V. (1995) Support-Vector Networks. Machine Learning, 20, 273-297. https://doi.org/10.1007/BF00994018
[34]
Ballabio, D. and Consonni, V. (2013) Classification Tools in Chemistry. Part 1: Linear Models. PLS-DA. Analytical Methods, 5, 3790-3798. https://doi.org/10.1039/c3ay40582f
[35]
Shrestha, S., Deleuran, L.C. and Gislum, R. (2016) Classification of Different Tomato Seed Cultivars by Multispectral Visible-Near Infrared Spectroscopy and Chemometrics. Journal of Spectral Imaging, 5, Article ID: a1. https://doi.org/10.1255/jsi.2016.a1
[36]
Römer, C., Bürling, K., Hunsche, M., Rumpf T., Noga, G. and Plümer, L. (2011) Robust Fitting of Fluorescence Spectra for Pre-Symptomatic Wheat Leaf Rust Detection with Support Vector Machines. Computers and Electronics in Agriculture, 79, 180-188. https://doi.org/10.1016/j.compag.2011.09.011
