Assessment of Bacterial Wildfire Infection in Soybean Cultivars by Hyperspectral Imaging

Propey Then; Liny Lay; Amit Ghimire; Mohammad Shafiqul Islam; Yoonha Kim

doi:10.22698/jales.20230021

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2023 Vol.35, Issue 3 Preview Page Next Page

Research Article

Assessment of Bacterial Wildfire Infection in Soybean Cultivars by Hyperspectral Imaging

30 September 2023. pp. 259-274

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Abstract

Soybean [Glycine max (L.) Merr.] is an economically important protein and oilseed legume. Bacterial wildfire, a major biotic stress caused by Pseudomonas syringae pv. tabaci (Pst), has steadily increased in occurrence and severity across several soybean cultivars, presenting a significant risk to soybean production. This study aimed to investigate and evaluate the effect of bacterial wildfires on selected soybean cultivars using imaging approaches, such as hyperspectral and digital imaging with red, green, and blue methods. A significant difference between the tolerance and sensitivity of the plants was observed in this study. In the present study, the disease incidence (DI) exhibited was observed in all soybean cultivars 2 days after inoculation (DAI). Taekwang (52.41%) showed more symptoms with a sensitive response to disease infection and showed a significant difference in DI compared with the other cultivars, whereas Daephung 2, Seunphung, and Soyun showed fewer symptoms on leaves (< 10%). Additionally, among the three vegetation indices (VIs), the normalized difference vegetation, photochemical reflectance, and anthocyanin reflectance indices were significantly correlated with DI at 3 DAI. Hence, these VIs can be used for the early detection of bacterial wildfires; however, further research is needed for more precise and accurate disease detection.

Keywords

Bacterial wildfire

Disease incidence

Hyperspectral imaging

Soybean

References

Agrios, G. N. (2005) Plant pathology (5^th ed). p.628. Elsevier.

Ali, L., Jo, H., Choi, S. M., Kim, Y., Song, J. T., Lee, J.-D. (2022) Comparison of Hyperspectral Imagery and Physiological Characteristics of Bentazone-Tolerant and -Susceptible Soybean Cultivars. Agronomy 12:2241. 10.3390/agronomy12102241

Allington, W. B. (1945) Wildfire disease of Soybeans. Phytopathol 35:857-869.

Araujo, J. M. M., Peixoto, Z. M. A. (2019) A new proposal for automatic identification of multiple soybean diseases. Comput Electron Agric 167:105060. 10.1016/j.compag.2019.105060

Bajwa, S. G., Rupe, J. C., Mason, J. (2017) Soybean Disease Monitoring with Leaf Reflectance. Remote Sens 9:127. 10.3390/rs9020127

Bock, C. H., Poole, G. H., Parker, P. E., Gottwald, T. R. (2010) Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging. Crit Rev Plant Sci 29:59-107. 10.1080/07352681003617285

Chaudhary, P., Chaudhari, A. K., Cheeran, A. N., Godara, S. (2012) Color transform based approach for disease spot detection on plant leaf. Int J Comput Sci Telecom 3:65-70.

Cui, D., Zhang, Q., Li, M., Zhao, Y., Hartman, G. L. (2009) Detection of soybean rust using a multispectral image sensor. Sens Instrum Food Qual Saf 3:49-56. 10.1007/s11694-009-9070-8

Deshpande, T., Sengupta, S., Raghuvanshi, K. S. (2014) Grading and identification of disease in pomegranate leaf and fruit. Int J Comput Sci Inf Technol 5:4638-4645.

Gamon, J. A., Berry, J. A. (2012) Facultative and constitutive pigment effects on the Photochemical Reflectance Index (PRI) in sun and shade conifer needles. Israel J Plant Sci 60:85-95. 10.1560/IJPS.60.1-2.85

Gamon, J. A., Penuelas, J., Field, C. B. (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41:35-44. 10.1016/0034-4257(92)90059-S

Gessesse, A. A., Melesse, A. M. (2019) Temporal relationships between time series CHIRPS-rainfall estimation and eMODIS-NDVI satellite images in Amhara Region, Ethiopia. In Extreme hydrology and climate variability. pp.81-92. Elsevier. 10.1016/B978-0-12-815998-9.00008-7

Gitelson, A. A., Chivkunova, O. B., Merzlyak, M. N. (2009) Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. Am J Bot 96:1861-1868. 10.3732/ajb.080039521622307

Gitelson, A. A., Merzlyak, M. N., Chivkunova, O. B. (2001) Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem Photobio 74:38-45. 10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;211460535

Gröll, K., Graeff, S., Claupein, W. (2007) Use of Vegetation indices to detect plant diseases. Agrarinformatik im Spannungsfeld zwischen Regionalisierung und globalen Wertschöpfungsketten-Referate der 27. GIL Jahrestagung.

Hong, J. K., Sung, C. H., Kim, D. K., Yun, H. T., Jung, W., Kim, K. D. (2012) Differential effect of delayed planting on soybean cultivars varying in susceptibility to bacterial pustule and wildfire in Korea. Crop Prot 42:244-249. 10.1016/j.cropro.2012.07.014

Huang, P., Luo, X., Jin, J., Wang, L., Zhang, L., Liu, J., Zhang, Z. (2018) Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor. Sensors 18:2711. 10.3390/s1808271130126148PMC6111299

Jadhav, S. B., Patil, S. B. (2015) Grading of soybean leaf disease based on segmented image using k-means clustering. Int J Adv Res Electr Commun Eng 4:1816-1822.

Kang, I. J., Kim, S. H., Seo, Y. W., Seo, M. J., Shim, H. K., Shin, D. B., Heu, S. (2015) Effective selection of soybean cultivars to wildfire disease pathogen Pseudomonas amygdali pv. tabaci. J Crop Sci Biotechnol 18:279-284. 10.1007/s12892-015-0104-y

Kang, I. J., Kim, S. H., Shim, H. K., Seo, M. J., Shin, D. B., Roh, J. H., Heu, S. (2016) Incidence of wildfire disease on soybean of Korea during 2014-2015. Research in Plant Disease 22:38-43. 10.5423/RPD.2016.22.1.38

Karmokar, B. C., Ullah, M. S., Siddiquee, M. K., Alam, K. M. R. (2015) Tea leaf diseases recognition using neural network ensemble. Int J Comput Appl 114:27-30. 10.5120/20071-1993

Khirade, S. D., Patil, A. B. (2015) Plant disease detection using image processing. Paper presented at the 2015 International Conference on Computing Communication Control and Automation. pp.768-771. IEEE. 10.1109/ICCUBEA.2015.153

Kior, A., Sukhov, V., Sukhova, E. (2021) Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors. Photonics 8:582. 10.3390/photonics8120582

Kohzuma, K., Tamaki, M., Hikosaka, K. (2021) Corrected photochemical reflectance index (PRI) is an effective tool for detecting environmental stresses in agricultural crops under light conditions. J Plant Res 134:683-694. 10.1007/s10265-021-01316-134081252

Krezhova, D., Kirova, E. (2011) Hyperspectral remote sensing of the impact of environmental stresses on nitrogen fixing soybean plants (Glycine max L.). Paper presented at the Proceedings of 5th International Conference on Recent Advances in Space Technologies (RAST2011). pp.172-177. IEEE. 10.1109/RAST.2011.5966816

Kumar, R., Pathak, S., Prakash, N., Priya, U., Ghatak, A. (2021) Application of Spectroscopic Techniques in Early Detection of Fungal Plant Pathogens. In Diagnostics of Plant Diseases: IntechOpen. 10.5772/intechopen.97535

Lay, L., Lee, H. S., Tayade, R., Ghimire, A., Chung, Y. S., Yoon, Y., Kim, Y. (2023) Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging. Plants 12:901. 10.3390/plants1204090136840248PMC9967622

Lee, M. A., Huang, Y., Yao, H., Thomson, S. J., Bruce, L. M. (2014) Determining the Effects of Storage on Cotton and Soybean Leaf Samples for Hyperspectral Analysis. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2562-2570. 10.1109/JSTARS.2014.2330521

Li, L., Zhang, Q., Huang, D. (2014) A review of imaging techniques for plant phenotyping. Sensors 14:20078-20111. 10.3390/s14112007825347588PMC4279472

Lin, F., Chhapekar, S. S., Vieira, C. C., Da Silva, M. P., Rojas, A., Lee, D., Liu, N., Pardo, E. M., Lee, Y. C., Dong, Z., Pinheiro, J. B., Ploper, L. D., Rupe, J., Chen, P., Wang, D., Nguyen, H. T., (2022) Breeding for disease resistance in soybean: a global perspective. Theor Appl Genet 135:3773-3872. 10.1007/s00122-022-04101-335790543PMC9729162

Mahlein, A.-K. (2016) Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping. Plant disease 100:241-251. 10.1094/PDIS-03-15-0340-FE30694129

Meena, S. V., Dhaka, V. S., Sinwar, D. (2020) Exploring the Role of Vegetation Indices in Plant Diseases Identification. Paper presented at the 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). pp.372-377. IEEE. 10.1109/PDGC50313.2020.9315814

Mutava, R. N., Prince, S. J. K., Syed, N. H., Song, L., Valliyodan, B., Chen, W., Nguyen, H. T. (2015) Understanding abiotic stress tolerance mechanisms in soybean: A comparative evaluation of soybean response to drought and flooding stress. Plant Physiol Biochem 86:109-120. 10.1016/j.plaphy.2014.11.01025438143

Myung, I. S., Kim, J. W., An, S. H., Lee, J. H., Kim, S. K., Lee, Y. K., Kim, W. G. (2009) Wildfire of soybean caused by Pseudomonas syringae pv. tabaci, a new disease in Korea. Plant Dis 93:1214-1214. 10.1094/PDIS-93-11-1214A30754606

Nagasubramanian, K., Jones, S., Singh, A. K., Singh, A., Ganapathysubramanian, B., Sarkar, S. (2018) Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps. arXiv preprint arXiv: 1804.08831.

Ngwangum, N. J., Tayade, R., Liny, L., Yoon, J. B., Chung, Y. S., Kim, Y. (2022) Utilization of Imaging Data from Different Sources for Bacterial and Fungal Diseases Detection in Major Crops in the Digital Era: A Review. J Agric Life Environ Sci 34:97-117.

Oh, D., Ryu, J. H., Oh, S., Jeong, H., Park, J., Jeong, R. D., Kim, W., Cho, J. (2018) Optical Sensing for Evaluating the Severity of Disease Caused by Cladosporium sp. in Barley under Warmer Conditions. Plant Pathol J 34:236-240. 10.5423/PPJ.NT.11.2017.024729887779PMC5985649

Padmavathi, K., Thangadurai, K. (2016) Implementation of RGB and grayscale images in plant leaves disease detection-comparative study. Indian J Sci Technol 9:1-6. 10.17485/ijst/2016/v9i6/77739

Peñuelas, J., Marino, G., Llusia, J., Morfopoulos, C., Farré-Armengol, G., Filella, I. (2013) Photochemical reflectance index as an indirect estimator of foliar isoprenoid emissions at the ecosystem level. Nat Commun 4:2604. 10.1038/ncomms360424108005

Polivova, M., Brook, A. (2021) Detailed Investigation of Spectral Vegetation Indices for Fine Field-Scale Phenotyping. In Vegetation Index and Dynamics: IntechOpen. 10.5772/intechopen.96882

Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W. (1974) Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ 351:309.

Sherwood, R. T., Berg, C. C., Hoover, M. R., Zeiders, K. E. (1983) Illusions in visual assessment of Stagonospora leaf spot of orchardgrass. Phytopathol 73:173-177. 10.1094/Phyto-73-173

Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., Batra, N. (2020) PlantDoc: A Dataset for Visual Plant Disease Detection. Paper presented at the Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Hyderabad, India. pp.249-253. 10.1145/3371158.3371196

Solutions, H. G., Solutions, E., Learning, E. D., SARscape, E., Data, E. (2013) Vegetation analysis: using vegetation indices in ENVI. Harris Geospational Solutions [Online].

Sun, S., Liu, C., Duan, C., Zhu, Z. (2021) Wildfire, a new bacterial disease of mung bean, caused by Pseudomonas syringae pv. tabaci. J Plant Pathol 103:649-653. 10.1007/s42161-021-00823-3

Tichkule, S. K., Gawali, D. H. (2016) Plant diseases detection using image processing techniques. Paper presented at the 2016 Online International Conference on Green Engineering and Technologies (IC-GET). pp.1-6. IEEE. 10.1109/GET.2016.7916653

Xie, C., Yang, C., He, Y. (2017) Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities. Comput Electron Agric 135:154-162. 10.1016/j.compag.2016.12.015

Information

Publisher :Agriculture and Life Sciences Research Institute, Kangwon National University
Publisher(Ko) :강원대학교 농업생명과학대학 농업생명과학연구원
Journal Title :Journal of Agricultural, Life and Environmental Sciences
Journal Title(Ko) :농업생명환경연구
Volume : 35
No :3
Pages :259-274
Received Date : 2023-06-02
Revised Date : 2023-08-30
Accepted Date : 2023-09-04
DOI :https://doi.org/10.22698/jales.20230021

Journal of Agricultural, Life and Environmental Sciences 농업생명환경연구 ISSN:2233-8322(Print) 2508-870X(Online)

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