Abstract
Southern corn leaf blight (SCLB) seriously threatens corn production. The timely and accurate monitoring of SCLB conditions (e.g., detection during the asymptomatic stage and severity classification during the symptomatic stage) is valuable for precision agriculture, because the application of pesticides depends on disease conditions. Compared with time-consuming and laborious field surveys, spectroscopy is a promising tool for plant disease monitoring. The unique advantages of combining multiple spectral enhancement features for monitoring rice and wheat diseases have been recognized. However, physiological and biochemical differences between maize leaves and rice and wheat leaves, along with the specific spectral response of SCLB, are likely to affect the performance of combining multiple spectral enhancement features. In addition, similar previous studies have not combined spectral slope features, i.e., first-order spectral derivatives (FSDs), with spectral bands (SBs) and spectral indices (SIs) and wavelet features (WFs) to improve plant disease detection. Thus, the performance of a method that combines FSDs, WFs, SBs, and SIs for SCLB asymptomatic detection, symptomatic detection, and symptomatic severity classification should be evaluated further. Here, the utility of combining SBs, SIs, WFs, and FSDs was quantified and evaluated in the asymptomatic detection, symptomatic detection, and symptomatic severity classification of SCLB. Various forms of spectral enhancement features that were sensitive to SCLB infection from the asymptomatic stage to the severe stage were first identified and combined using the RELIEF-F and sequential floating forward selection algorithms on the basis of two independent inoculation experiments. Finally, SCLB asymptomatic detection, symptomatic detection, and symptomatic severity classification models were developed and evaluated using the support vector machine algorithm. Results showed that combining FSDs with SBs, SIs, and WFs achieved the best performance in SCLB spectroscopic monitoring. (1) SCLB asymptomatic detection and symptomatic detection were moderately improved, i.e., overall accuracy (OA) and macro F1 (MF1) improved by ~ 1% to 2%. The OA of SCLB asymptomatic detection was 87.1% with an MF1 of 0.87, and that of symptomatic detection was 93.1% with an MF1 of 0.93. (2) SCLB symptomatic severity classification was significantly improved, i.e., OA and MF1 improved by ~ 7%. The OA of severity classification was 81.8% with am MF1 of 0.82. This study demonstrated that the complementary relationships among SBs, SIs, WFs, and FSDs could effectively improve SCLB spectroscopic monitoring. The proposed method provides a novel tool for large-scale SCLB spectroscopic monitoring. It has broad implications for assisting management decisions (i.e., when and where to apply pesticides and how much to apply) in precision agriculture.
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References
Abdulridha, J., Ampatzidis, Y., Roberts, P., & Kakarla, S. C. (2020). Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence. Biosystems Engineering, 197, 135–148. https://doi.org/10.1016/j.biosystemseng.2020.07.001
Al-Saddik, H., Laybros, A., Billiot, B., & Cointault, F. (2018). Using image texture and spectral reflectance analysis to detect yellowness and esca in grapevines at leaf-level. Remote Sensing. https://doi.org/10.3390/rs10040618
Al-Saddik, H., Simon, J. C., & Cointault, F. (2019). Assessment of the optimal spectral bands for designing a sensor for vineyard disease detection: The case of "Flavescence doree’. Precision Agriculture, 20, 398–422. https://doi.org/10.1007/s11119-018-9594-1
Appeltans, S., Pieters, J. G., & Mouazen, A. M. (2021). Potential of laboratory hyperspectral data for in-field detection of Phytophthora infestans on potato. Precision Agriculture. https://doi.org/10.1007/s11119-021-09865-0
Azra, A., Hussain, S., Freed, A., Ullah, S., Shah, S. U., & Iqbal, M. (2021). Distribution pattern of southern corn leaf blight in khyber pakhtunkhwa-pakistan and its pcr based detection in asymptomatic leaves and plant debris. Pakistan Journal of Botany, 53, 1875–1882. https://doi.org/10.30848/pjb2021-5(44)
Bajwa, S. G., Rupe, J. C., & Mason, J. (2017). Soybean disease monitoring with leaf reflectance. Remote Sensing. https://doi.org/10.3390/rs9020127
Boochs, F., Kupfer, G., Dockter, K., & Kuhbauch, W. (1990). Shape of the red edge as vitality indicator for plants. International Journal of Remote Sensing, 11, 1741–1753. https://doi.org/10.1080/01431169008955127
Bruns, H. A. (2017). Southern corn leaf blight: A story worth retelling. Agronomy Journal, 109, 1218–1224. https://doi.org/10.2134/agronj2017.01.0006
Chen, T., Zhang, J., Chen, Y., Wan, S., & Zhang, L. (2019). Detection of peanut leaf spots disease using canopy hyperspectral reflectance. Computers and Electronics in Agriculture, 156, 677–683. https://doi.org/10.1016/j.compag.2018.12.036
Cheng, T., Rivard, B., & Sanchez-Azofeifa, A. (2011). Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sensing of Environment, 115, 659–670. https://doi.org/10.1016/j.rse.2010.11.001
Cheng, T., Rivard, B., Sanchez-Azofeifa, G. A., Feng, J., & Calvo-Polanco, M. (2010). Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 114, 899–910. https://doi.org/10.1016/j.rse.2009.12.005
Cho, M. A., & Skidmore, A. K. (2006). A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment, 101, 181–193. https://doi.org/10.1016/j.rse.2005.12.011
Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing, 20, 2741–2759. https://doi.org/10.1080/014311699211778
De Castro, A. I., Ehsani, R., Ploetz, R., Crane, J. H., & Abdulridha, J. (2015). Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado. Remote Sensing of Environment, 171, 33–44. https://doi.org/10.1016/j.rse.2015.09.011
Einzmann, K., Atzberger, C., Pinnel, N., Glas, C., Böck, S., Seitz, R., & Immitzer, M. (2021). Early detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2021.112676
Elvidge, C. D., & Chen, Z. (1995). Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment, 54, 38–48. https://doi.org/10.1016/0034-4257(95)00132-K
Fahey, T., Pham, H., Gardi, A., Sabatini, R., Stefanelli, D., Goodwin, I., & Lamb, D. W. (2021). Active and passive electro-optical sensors for health assessment in food crops. Sensors. https://doi.org/10.3390/s21010171
FAO (2018). FAOSTAT [WWW Document].
Filella, I., & Penuelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15, 1459–1470. https://doi.org/10.1080/01431169408954177
Gold, K. M. (2021). Plant disease sensing: Studying plant-pathogen interactions at scale. Msystems. https://doi.org/10.1128/mSystems.01228-21
Gregory, L. V., Ayers, J. E., & Nelson, R. R. (1978). Predicting yield losses in corn from southern corn leaf-blight. Phytopathology, 68, 517–521. https://doi.org/10.1094/Phyto-68-517
Guo, A., Huang, W., Ye, H., Dong, Y., Ma, H., Ren, Y., & Ruan, C. (2020). Identification of wheat yellow rust using spectral and texture features of hyperspectral images. Remote Sensing. https://doi.org/10.3390/rs12091419
He, L., Qi, S.-L., Duan, J.-Z., Guo, T.-C., Feng, W., & He, D.-X. (2021). Monitoring of wheat powdery mildew disease severity using multiangle hyperspectral remote sensing. Ieee Transactions on Geoscience and Remote Sensing, 59, 979–990. https://doi.org/10.1109/tgrs.2020.3000992
Horler, D. N. H., Dockray, M., & Barber, J. (1983). The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4, 273–288. https://doi.org/10.1080/01431168308948546
Hornero, A., Hernandez-Clemente, R., North, P. R. J., Beck, P. S. A., Boscia, D., Navas-Cortes, J. A., & Zarco-Tejada, P. J. (2020). Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111480
Huang, J., Liao, H., Zhu, Y., Sun, J., Sun, Q., & Liu, X. (2012). Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis). Computers and Electronics in Agriculture, 82, 100–107. https://doi.org/10.1016/j.compag.2012.01.002
Huang, W., Guan, Q., Luo, J., Zhang, J., Zhao, J., Liang, D., Huang, L., & Zhang, D. (2014). New optimized spectral indices for identifying and monitoring winter wheat diseases. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2516–2524. https://doi.org/10.1109/jstars.2013.2294961
Huang, W., Lamb, D. W., Niu, Z., Zhang, Y., Liu, L., & Wang, J. (2007). Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture, 8, 187–197. https://doi.org/10.1007/s11119-007-9038-9
Huang, Y., Li, Z., Risinger, A. L., Enslow, B. T., Zeman, C. J., Gong, J., Yang, Y., & Schanze, K. S. (2019). Fluorescence spectral shape analysis for nucleotide identification. Proceedings of the National Academy of Sciences of the United States of America, 116, 15386–15391. https://doi.org/10.1073/pnas.1820713116
Josephson, L.M., Graves, C.R., Kincer, H.C., & Hilty, J.W. (1971). Reductions in yield of corn from southern corn leaf blight. Plant Disease Reporter, 55, 115–+.
Kuska, M., Wahabzada, M., Leucker, M., Dehne, H.-W., Kersting, K., Oerke, E.-C., Steiner, U., & Mahlein, A.-K. (2015). Hyperspectral phenotyping on the microscopic scale: Towards automated characterization of plant-pathogen interactions. Plant Methods. https://doi.org/10.1186/s13007-015-0073-7
Li, D., Chen, J. M., Zhang, X., Yan, Y., Zhu, J., Zheng, H., Zhou, K., Yao, X., Tian, Y., Zhu, Y., Cheng, T., & Cao, W. (2020). Improved estimation of leaf chlorophyll content of row crops from canopy reflectance spectra through minimizing canopy structural effects and optimizing off-noon observation time. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2020.111985
Li, D., Cheng, T., Jia, M., Zhou, K., Lu, N., Yao, X., Tian, Y., Zhu, Y., & Cao, W. (2018). PROCWT: Coupling PROSPECT with continuous wavelet transform to improve the retrieval of foliar chemistry from leaf bidirectional reflectance spectra. Remote Sensing of Environment, 206, 1–14. https://doi.org/10.1016/j.rse.2017.12.013
Lin, Q., Huang, H., Chen, L., Wang, J., Huang, K., & Liu, Y. (2021). Using the 3D model RAPID to invert the shoot dieback ratio of vertically heterogeneous Yunnan pine forests to detect beetle damage. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2021.112475
Liu, Z., Hou, S., Rodrigues, O., Wang, P., Luo, D., Munemasa, S., Lei, J., Liu, J., Ortiz-Morea, F.A., Wang, X., Nomura, K., Yin, C., Wang, H., Zhang, W., Zhu-Salzman, K., He, S.Y., He, P., & Shan, L. (2022). Phytocytokine signalling reopens stomata in plant immunity and water loss. Nature, 605, 332–+.https://doi.org/10.1038/s41586-022-04684-3
Liu, W., Liu, Z., Huang, C., Lu, M., Liu, J., & Yang, Q. (2016). Statistics and analysis of crop yield losses caused by main diseases and insect pests in recent 10 years. Plant Protection, 42(1–9), 046.
Lu, J., Zhou, M., Gao, Y., & Jiang, H. (2018). Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves. Precision Agriculture, 19, 379–394. https://doi.org/10.1007/s11119-017-9524-7
Lv, Z., Meng, R., Man, J., Zeng, L., Wang, M., Xu, B., Gao, R., Sun, R., & Zhao, F. (2021). Modeling of winter wheat fAPAR by integrating Unmanned Aircraft Vehicle-based optical, structural and thermal measurement. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2021.102407
Ma, H., Huang, W., Dong, Y., Liu, L., & Guo, A. (2021). Using UAV-based hyperspectral imagery to detect winter wheat fusarium head blight. Remote Sensing. https://doi.org/10.3390/rs13153024
Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plumer, L., Steiner, U., & Oerke, E. C. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21–30. https://doi.org/10.1016/j.rse.2012.09.019
Mahlein, A. K., Steiner, U., Dehne, H. W., & Oerke, E. C. (2010). Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture, 11, 413–431. https://doi.org/10.1007/s11119-010-9180-7
Maimaitijiang, M., Sagan, V., Sidike, P., Maimaitiyiming, M., Hartling, S., Peterson, K. T., Maw, M. J. W., Shakoor, N., Mockler, T., & Fritschi, F. B. (2019). Vegetation index weighted canopy volume model (CVMVI) for soybean biomass estimation from unmanned aerial system-based RGB imagery. Isprs Journal of Photogrammetry and Remote Sensing, 151, 27–41. https://doi.org/10.1016/j.isprsjprs.2019.03.003
Meng, R., & Dennison, P. E. (2015). Spectroscopic analysis of green, desiccated and dead tamarisk canopies. Photogrammetric Engineering and Remote Sensing, 81, 199–207. https://doi.org/10.14358/pers.81.3.199
Meng, R., Dennison, P. E., Zhao, F., Shendryk, I., Rickert, A., Hanavan, R. P., Cook, B. D., & Serbin, S. P. (2018). Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements. Remote Sensing of Environment, 215, 170–183. https://doi.org/10.1016/j.rse.2018.06.008
Meng, R., Lv, Z. G., Yan, J. B., Chen, G. S., Zhao, F., Zeng, L. L., & Xu, B. Y. (2020). Development of spectral disease indices for southern corn rust detection and severity classification. Remote Sensing, 12, 16. https://doi.org/10.3390/rs12193233
Mueller, D. S., Wise, K. A., Sisson, A. J., Allen, T. W., Bergstrom, G. C., Bosley, D. B., Bradley, C. A., Broders, K. D., Byamukama, E., Chilvers, M. I., Collins, A., Faske, T. R., Friskop, A. J., Heiniger, R. W., Hollier, C. A., Hooker, D. C., Isakeit, T., Jackson-Ziems, T. A., Jardine, D. J., … Warner, F. (2016). Corn yield loss estimates due to diseases in the United States and Ontario, Canada from 2012 to 2015. Plant Health Progress, 17, 211–222. https://doi.org/10.1094/php-rs-16-0030
Nutter, F. W., Teng, P. S., & Shokes, F. M. (1991). Disease assessment terms and concepts. Plant Disease, 75, 1187–1188.
Penuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indexes associated with physiological-changes in nitrogen-limited and water-limited sunflower leaves. Remote Sensing of Environment, 48, 135–146. https://doi.org/10.1016/0034-4257(94)90136-8
Pudil, P., Novovicova, J., & Kittler, J. (1994). Floating search methods in feature-selection. Pattern Recognition Letters, 15, 1119–1125. https://doi.org/10.1016/0167-8655(94)90127-9
Qian, S.-E. (2021). Hyperspectral satellites, evolution, and development history. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 7032–7056. https://doi.org/10.1109/jstars.2021.3090256
Ray, M., Ray, A., Dash, S., Mishra, A., Achary, K. G., Nayak, S., & Singh, S. (2017). Fungal disease detection in plants: Traditional assays, novel diagnostic techniques and biosensors. Biosensors and Bioelectronics, 87, 708–723. https://doi.org/10.1016/j.bios.2016.09.032
Safir, G. R., Svits, G. H., & Ellingbo, A. H. (1972). Spectral reflectance and transmittance of corn leaves infected with helminthosporium-maydis. Phytopathology, 62, 1210–1213. https://doi.org/10.1094/Phyto-62-1210
Scholkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New support vector algorithms. Neural Computation, 12, 1207–1245. https://doi.org/10.1162/089976600300015565
Shafri, H. Z. M., Anuar, M. I., Seman, I. A., & Noor, N. M. (2011). Spectral discrimination of healthy and Ganoderma-infected oil palms from hyperspectral data. International Journal of Remote Sensing, 32, 7111–7129. https://doi.org/10.1080/01431161.2010.519003
Shi, Y., Li, D., Yi, S., & Yan, C. (2019). Infrared spectroscopy analysis of biochemical changes of corn leaves infected by southern corn leaf blight disease. Laser & Optoelectronics Progress. CNKI:SUN:JGDJ.0.2019-08-030
Shirzadifar, A., Bajwa, S., Nowatzki, J., & Shojaeiarani, J. (2020). Development of spectral indices for identifying glyphosate-resistant weeds. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2020.105276
Smigaj, M., Gaulton, R., Suarez, J. C., & Barr, S. L. (2019). Combined use of spectral and structural characteristics for improved red band needle blight detection in pine plantation stands. Forest Ecology and Management, 434, 213–223. https://doi.org/10.1016/j.foreco.2018.12.005
Tian, L., Xue, B., Wang, Z., Li, D., Yao, X., Cao, Q., Zhu, Y., Cao, W., & 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, 112350. https://doi.org/10.1016/j.rse.2021.112350
Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79, 61–78. https://doi.org/10.1175/1520-0477(1998)079%3c0061:Apgtwa%3e2.0.Co;2
Tsai, F., & Philpot, W. (1998). Derivative analysis of hyperspectral data. Remote Sensing of Environment, 66, 41–51. https://doi.org/10.1016/s0034-4257(98)00032-7
Vogelmann, J. E., Rock, B. N., & Moss, D. M. (1993). Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14, 1563–1575. https://doi.org/10.1080/01431169308953986
Wang, D. Y., Vinson, R., Holmes, M., Seibel, G., Bechar, A., Nof, S., & Tao, Y. (2019). Early detection of tomato spotted wilt virus by hyperspectral imaging and outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). Scientific Reports. https://doi.org/10.1038/s41598-019-40066-y
Wang, X., Qiming, J. I. N., Jie, S. H. I., Zuoying, W., & Xiao, L. I. (2006). The status of maize diseases and the possible effect of variety resistance on disease occurrence in the future. Acta Phytopathologica Sinica, 36, 1–11. https://doi.org/10.13926/j.cnki.apps.2006.01.001
Yuan, L., Huang, Y., Loraamm, R. W., Nie, C., Wang, J., & Zhang, J. (2014). Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects. Field Crops Research, 156, 199–207. https://doi.org/10.1016/j.fcr.2013.11.012
Zarco-Tejada, P. J., Camino, C., Beck, P. S. A., Calderon, R., Hornero, A., Hernandez-Clemente, R., Kattenborn, T., Montes-Borrego, M., Susca, L., Morelli, M., Gonzalez-Dugo, V., North, P. R. J., Landa, B. B., Boscia, D., Saponari, M., & Navas-Cortes, J. A. (2018). Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4, 432–439. https://doi.org/10.1038/s41477-018-0189-7
Zarco-Tejada, P. J., Poblete, T., Camino, C., Gonzalez-Dugo, V., Calderon, R., Hornero, A., Hernandez-Clemente, R., Roman-ecija, M., Velasco-Amo, M. P., Landa, B. B., Beck, P. S. A., Saponari, M., Boscia, D., & Navas-Cortes, J. A. (2021). Divergent abiotic spectral pathways unravel pathogen stress signals across species. Nature Communications. https://doi.org/10.1038/s41467-021-26335-3
Zarco-Tejada, P. J., Pushnik, J. C., Dobrowski, S., & Ustin, S. L. (2003). Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment, 84, 283–294. https://doi.org/10.1016/S0034-4257(02)00113-X
Zhang, G., Xu, T., & Tian, Y. (2022). Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages. Plant Methods. https://doi.org/10.1186/s13007-022-00955-2
Zhang, J., Huang, Y., Pu, R., Gonzalez-Moreno, P., Yuan, L., Wu, K., & Huang, W. (2019). Monitoring plant diseases and pests through remote sensing technology: A review. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2019.104943
Zhang, J.-C., Pu, R.-L., Wang, J.-H., Huang, W.-J., Yuan, L., & Luo, J.-H. (2012). Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Computers and Electronics in Agriculture, 85, 13–23. https://doi.org/10.1016/j.compag.2012.03.006
Zhang, J., Wang, N., Yuan, L., Chen, F., & Wu, K. (2017). Discrimination of winter wheat disease and insect stresses using continuous wavelet features extracted from foliar spectral measurements. Biosystems Engineering, 162, 20–29. https://doi.org/10.1016/j.biosystemseng.2017.07.003
Zhang, J., Yuan, L., Pu, R., Loraamm, R. W., Yang, G., & Wang, J. (2014). Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat. Computers and Electronics in Agriculture, 100, 79–87. https://doi.org/10.1016/j.compag.2013.11.001
Zhang, N., Yang, G., Zhao, C., Zhang, J., Yang, X., Pan, Y., Huang, W., Xu, B., Li, M., Zhu, X., & Li, Z. (2021). Progress and prospects of hyperspectral remote sensing technology for crop diseases and pests. Journal of Remote Sensing, 25, 403–422.
Zhao, J., Huang, L., Huang, W., Zhang, D., Yuan, L., Zhang, J., & Liang, D. (2014). Hyperspectral measurements of severity of stripe rust on individual wheat leaves. European Journal of Plant Pathology, 139, 401–411. https://doi.org/10.1007/s10658-014-0397-6
Zheng, Q., Huang, W., Cui, X., Dong, Y., Shi, Y., Ma, H., & Liu, L. (2019). Identification of wheat yellow rust using optimal three-band spectral indices in different growth stages. Sensors. https://doi.org/10.3390/s19010035
Zhong, Y., Wang, X., Wang, S., & Zhang, L. (2021). Advances in spaceborne hyperspectral remote sensing in China. Geo-Spatial Information Science, 24, 95–120. https://doi.org/10.1080/10095020.2020.1860653
Zhou, R.-Q., Jin, J.-J., Li, Q.-M., Su, Z.-Z., Yu, X.-J., Tang, Y., Luo, S.-M., He, Y., & Li, X.-L. (2019). Early detection of Magnaporthe oryzae-Infected barley leaves and lesion visualization based on hyperspectral imaging. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2018.01962
Acknowledgements
This work was supported by the Key Research and Development Program of Heilongjiang, China (Grant No. 2022ZX01A25), Cooperative Funding between Huazhong Agricultural University and Shenzhen Institute of Agricultural Genomics (Grant No. SZYJY2022014), the Fundamental Research Funds for the Central Universities (Grant No. 2662022ZHYJ002), the HZAU research startup fund (Grant No. 11041810340; Grant No. 11041810341), and the National Natural Science Foundation of China (Grant No. 41901382). We also would like to thank the following people who helped for data collection: Renjie Gao, Yigui Liao, Rui Sun, Chenxi Du, Li Guan, Xian Xu, and Heping Zhang.
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Lv, Z., Meng, R., Chen, G. et al. Combining multiple spectral enhancement features for improving spectroscopic asymptomatic detection and symptomatic severity classification of southern corn leaf blight. Precision Agric 24, 1593–1618 (2023). https://doi.org/10.1007/s11119-023-10010-2
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DOI: https://doi.org/10.1007/s11119-023-10010-2