Hyperspectral signatures reveal hidden stress in soybean caused by residual nicosulfuron

Shufei Hao , Zekai Huang , Olga A. Glazunova , Konstantin V. Moiseenko , Chi Wu , Xingang Liu

New Plant Protection ›› 2026, Vol. 3 ›› Issue (1) : e70038

PDF (2011KB)
New Plant Protection ›› 2026, Vol. 3 ›› Issue (1) :e70038 DOI: 10.1002/npp2.70038
ORIGINAL PAPER
Hyperspectral signatures reveal hidden stress in soybean caused by residual nicosulfuron
Author information +
History +
PDF (2011KB)

Abstract

Residual nicosulfuron in soil can induce persistent phytotoxic effects on subsequent soybean (Glycine max L.) crops, yet the physiological trajectory from early stress to photosynthetic collapse remains unclear. Here, we established a multimodal phenotyping framework that integrates hyperspectral reflectance, ultraviolet-excited multichannel fluorescence, and chlorophyll fluorescence quenching imaging to capture stage-specific soybean responses under nicosulfuron stress. Early pigment disruption was marked by a 12.3% decrease in the chlorophyll index 3, while metabolic activation was indicated by an increase in fluorescence at 450 nm to 3.26 ± 0.16 at 100 μg/kg, compared to 2.05 ± 0.05 in the control group. These were followed by photosystem II (PSII) dysfunction, including a decline in the maximum quantum efficiency of PSII under light adaptation (Fv/Fm_Lss) from 0.71 to 0.37, a 102% increase in non-photochemical quenching under light adaptation (NPQ_Lss), and a 38% reduction in maximum fluorescence under light adaptation (Fm_Lss), reflecting photosystem disintegration. Such impairments culminated in a marked elevation of the Integrated Biomarker Response version 2. This study identifies a distinct injury-regulation-collapse pathway and phase-specific markers, while the integrated imaging approach enables earlier, non-invasive detection and dynamic monitoring, providing a mechanistic basis for risk assessment in herbicide-impacted rotation systems.

Keywords

chlorophyll fluorescence / hidden stress detection / hyperspectral imaging / multi-modal phenotyping / residual nicosulfuron / soybean

Cite this article

Download citation ▾
Shufei Hao, Zekai Huang, Olga A. Glazunova, Konstantin V. Moiseenko, Chi Wu, Xingang Liu. Hyperspectral signatures reveal hidden stress in soybean caused by residual nicosulfuron. New Plant Protection, 2026, 3 (1) : e70038 DOI:10.1002/npp2.70038

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Parven, A., Meftaul, I. M., Venkateswarlu, K., & Megharaj, M. (2025). Herbicides in modern sustainable agriculture: Environmental fate, ecological implications, and human health concerns. International Journal of Environmental Science and Technology, 22(2), 1181-1202. https://doi.org/10.1007/s13762-024-05818-y

[2]

Knuth, D., Gai, L., Silva, V., Harkes, P., Hofman, J., Šudoma, M., Bílková, Z., Alaoui, A., Mandrioli, D., Pasković, I., Polić Pasković, M., Baldi, I., Bureau, M., Alcon, F., Contreras, J., Glavan, M., Abrantes, N., Campos, I., Norgaard, T., … Geissen, V. (2024). Pesticide residues in organic and conventional agricultural soils across Europe: Measured and predicted concentrations. Environmental Science & Technology, 58(15), 6744-6752. https://doi.org/10.1021/acs.est.3c09059

[3]

Yang, M., Wang, Y., Yang, G., Wang, Y., Liu, F., & Chen, C. (2024). A review of cumulative risk assessment of multiple pesticide residues in food: Current status, approaches and future perspectives. Trends in Food Science and Technology, 144, 104340. https://doi.org/10.1016/j.tifs.2024.104340

[4]

Tang, T., Lei, C. T., Lv, L., Wang, F. D., Cheng, X., Gao, M. Y., Lou, J. J., Zhu, Y. K., Xu, N. H., Zhang, Q., Lu, T., & Qian, H. F. (2025). Systematic assessments of ecological and health risks of soil pesticide residues. Environmental Pollution, 375, 126348. https://doi.org/10.1016/j.envpol.2025.126348

[5]

Virk, A. L., Shakoor, A., Ahmad, N., Du, H., Chang, S. X., & Cai, Y. (2025). Organic amendments restore soil biological properties under pesticides application. Pesticide Biochemistry and Physiology, 210, 106394. https://doi.org/10.1016/j.pestbp.2025.106394

[6]

Fan, Q., Shen, Y., Yang, Y., & Zhang, Q. (2024). A review of remediation strategies for diphenyl ether herbicide contamination. Toxics, 12(6), 397. https://doi.org/10.3390/toxics12060397

[7]

Jursík, M., Kolárová, M., & Kučera, J. (2025). Effect of residues of acetolactate synthase inhibiting herbicides in soil on oil-seed rape and sugar beet. Crop Protection, 197, 107290. https://doi.org/10.1016/j.cropro.2025.107290

[8]

Manfo, F. P. T., Nimako, C., Nantia, E. A., Suh, C. F., Chenwi, S. P., Cho-Ngwa, F., Moundipa, P. F., Nakayama, S. M. M., Ishizuka, M., & Ikenaka, Y. (2024). Exposure of male farmers and nonfarmers to neonicotinoid pesticides in the South-West and Littoral Regions of Cameroon: A comparative study. Environmental Toxicology and Chemistry, 43(5), 952-964. https://doi.org/10.1002/etc.5842

[9]

Grint, K. R., Proctor, C., DeWerff, R., Smith, D. H., Arneson, N. J., Arriaga, F., Stoltenberg, D., & Werle, R. (2022). Low carryover risk of corn and soybean herbicides across soil management practices and environments. Weed Technology, 36(1), 160-167. https://doi.org/10.1017/wet.2021.97

[10]

Chen, F., Wu, C., Wang, Y., Glazunova, O. A., Moiseenko, K. V., Zhang, L., Mao, L., Zhu, L., & Liu, X. (2025). Phytotoxicity of fluridone and emerging transformation products in agricultural soils: Insights into molecular interactions and photosynthetic disruptions in maize. Journal of Hazardous Materials, 496, 139252. https://doi.org/10.1016/j.jhazmat.2025.139252

[11]

Wu, C., Wang, Y., Clarke, J. L., Su, H., Wang, L., Glazunova, O. A., Moiseenko, K. V., Zhang, L., Mao, L., Zhu, L., & Liu, X. (2025). Biochar enhances the sorption and degradation of fluridone and its main metabolite in soil: Insights into biodegradation potential and remediation of microbial communities. Biochar, 7(1), 81. https://doi.org/10.1007/s42773-025-00469-9

[12]

Zhao, B. C., Xu, X., Li, B. H., Qi, Z. Z., Huang, J. A., Hu, A. L., Wang, G. Q., & Liu, X. M. (2023). Target-site mutation and enhanced metabolism endow resistance to nicosulfuron in a Digitaria sanguinalis population. Pesticide Biochemistry and Physiology, 194, 105488. https://doi.org/10.1016/j.pestbp.2023.105488

[13]

Choe, E., & Williams, M. M. (2020). Expression and comparison of sweet corn CYP81A9s in relation to nicosulfuron sensitivity. Pest Management Science, 76(9), 3012-3019. https://doi.org/10.1002/ps.5848

[14]

Huang, Z., Huang, H., Chen, J., Chen, J., Wei, S., & Zhang, C. (2019). Nicosulfuron-resistant Amaranthus retroflexus L. in Northeast China. Crop Protection, 122, 79-83. https://doi.org/10.1016/j.cropro.2019.04.024

[15]

Wright, T. R., Bascomb, N. F., Sturner, S. F., & Penner, D. (1998). Biochemical mechanism and molecular basis for ALS-inhibiting herbicide resistance in sugarbeet (Beta vulgaris) somatic cell selections. Weed Science, 46(1), 13-23. https://doi.org/10.1017/S0043174500090111

[16]

Brown, L. R., Robinson, D. E., Young, B. G., Loux, M. M., Johnson, W. G., Nurse, R. E., Swanton, C. J., & Sikkema, P. H. (2009). Response of corn to simulated glyphosate drift followed by in-crop herbicides. Weed Technology, 23(1), 11-16. https://doi.org/10.1614/WT-08-067.1

[17]

Wang, K., Ren, Y., Pan, X., Wu, X., Xu, J., Zheng, Y., & Dong, F. (2025). Insights on persistent herbicides in cropland soils in northern China: Occurrence, ecological risks, and phytotoxicity to subsequent crops. Journal of Hazardous Materials, 490, 137794. https://doi.org/10.1016/j.jhazmat.2025.137794

[18]

Silva, A. F. M., Albrecht, A. J. P., Silva, G. S., Kashivaqui, E. S. F., Albrecht, L. P., & Victoria, R. (2019). Rates of nicosulfuron applied in glyphosate-tolerant and sulfonylurea-tolerant soybean. Planta Daninha, 37, e0372019. https://doi.org/10.1590/S0100-83582019370100010

[19]

Brazier-Hicks, M., Franco-Ortega, S., Watson, P., Rougemont, B., Cohn, J., Dale, R., Hawkes, T. R., Goldberg-Cavalleri, A., Onkokesung, N., & Edwards, R. (2022). Characterization of cytochrome P450s with key roles in determining herbicide selectivity in maize. ACS Omega, 7(20), 17416-17431. https://doi.org/10.1021/acsomega.2c01705

[20]

Bierman, A., LaPlumm, T., Cadle-Davidson, L., Gadoury, D., Martinez, D., Sapkota, S., & Rea, M. (2019). A high-throughput phenotyping system using machine vision to quantify severity of grapevine powdery mildew. Plant Phenomics, 2019, 9209727. https://doi.org/10.34133/2019/9209727

[21]

Wang, J., Gao, H., Guo, Z. Q., Meng, Y. Y., Yang, M., Li, X. L., & Yang, Q. (2021). Adaptation responses in C4 photosynthesis of sweet maize (Zea mays L.) exposed to nicosulfuron. Ecotoxicology and Environmental Safety, 214, 112096. https://doi.org/10.1016/j.ecoenv.2021.112096

[22]

Xiao, T., Yang, L., He, X., Wang, L., Zhang, D., Cui, T., Zhang, K., Li, H., Li, Z., & Dong, J. (2025). Assessing the ecotoxicological risk of nicosulfuron on maize using multi-source phenotype data and hyperspectral imaging. Ecotoxicology and Environmental Safety, 295, 118176. https://doi.org/10.1016/j.ecoenv.2025.118176

[23]

Zhang, J. N., Feng, X. P., Wu, Q. G., Yang, G. F., Tao, M. Z., Yang, Y., & He, Y. (2022). Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning. Plant Methods, 18(1), 49. https://doi.org/10.1186/s13007-022-00882-2

[24]

Rossini, M., Nedbal, L., Guanter, L., Ac, A., Alonso, L., Burkart, A., Cogliati, S., Colombo, R., Damm, A., Drusch, M., Hanus, J., Janoutova, R., Julitta, T., Kokkalis, P., Moreno, J., Novotny, J., Panigada, C., Pinto, F., Schickling, A., … Rascher, U. (2015). Red and far red Sun-induced chlorophyll fluorescence as a measure of plant photosynthesis. Geophysical Research Letters, 42(6), 1632-1639. https://doi.org/10.1002/2014GL062943

[25]

Hu, Y., Ma, B., Wang, H., Li, Y., Zhang, Y., & Yu, G. (2023). Non-destructive detection of different pesticide residues on the surface of Hami melon classification based on tHBA-ELM algorithm and SWIR hyperspectral imaging. Foods, 12(9), 1773. https://doi.org/10.3390/foods12091773

[26]

Wang, Z. M., Zheng, S., Wang, C. W., Zhang, L., Liu, Y., Wu, X. S., & Wang, S. (2024). A novel competitive color-tone change fluorescence immunochromatographic assay for the ultrasensitive detection of pesticide and veterinary drug residues. Sensors and Actuators B: Chemical, 417, 136125. https://doi.org/10.1016/j.snb.2024.136125

[27]

Yang, Y., Nan, R., Mi, T., Song, Y., Shi, F., Liu, X., Wang, Y., Sun, F., Xi, Y., & Zhang, C. (2023). Rapid and nondestructive evaluation of wheat chlorophyll under drought stress using hyperspectral imaging. International Journal of Molecular Sciences, 24(6), 5825. https://doi.org/10.3390/ijms24065825

[28]

Sanchez, W., Burgeot, T., & Porcher, J. M. (2013). A novel “Integrated Biomarker Response” calculation based on reference deviation concept. Environmental Science and Pollution Research, 20(5), 2721-2725. https://doi.org/10.1007/s11356-012-1359-1

[29]

Beliaeff, B., & Burgeot, T. (2002). Integrated biomarker response: A useful tool for ecological risk assessment. Environmental Toxicology and Chemistry, 21(6), 1316-1322. https://doi.org/10.1002/etc.5620210629

[30]

Wang, X., Sun, C., Gao, S., Wang, L., & Shuokui, H. (2001). Validation of germination rate and root elongation as indicator to assess phytotoxicity with Cucumis sativus. Chemosphere, 44(8), 1711-1721. https://doi.org/10.1016/S0045-6535(00)00520-8

[31]

Main, R., Felix, M. J. B., Watt, M. S., & Hartley, R. J. L. (2025). Early detection of herbicide-induced tree stress using uav-based multispectral and hyperspectral imagery. Forests, 16(8), 1240. https://doi.org/10.3390/f16081240

[32]

Zhao, D., Cao, Y., Li, J., Cao, Q., Li, J., Guo, F., Feng, S., & Xu, T. (2024). Early detection of rice leaf blast disease using unmanned aerial vehicle remote sensing: A novel approach integrating a new spectral vegetation index and machine learning. Agronomy, 14(3), 602. https://doi.org/10.3390/agronomy14030602

[33]

Javornik, T., Carović-Stanko, K., Gunjača, J., Vidak, M., & Lazarević, B. (2023). Monitoring drought stress in common bean using chlorophyll fluorescence and multispectral imaging. Plants, 12(6), 1386. https://doi.org/10.3390/plants12061386

[34]

Maimaitiyiming, M., Ghulam, A., Bozzolo, A., Wilkins, J. L., & Kwasniewski, M. T. (2017). Early detection of plant physiological responses to different levels of water stress using reflectance spectroscopy. Remote Sensing, 9(7), 745. https://doi.org/10.3390/rs9070745

[35]

Blackburn, G. A. (2007). Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany, 58(4), 855-867. https://doi.org/10.1093/jxb/erl123

[36]

Kalaji, H. M., Carpentier, R., Allakhverdiev, S. I., & Bosa, K. (2012). Fluorescence parameters as early indicators of light stress in barley. Journal of Photochemistry and Photobiology B: Biology, 112, 1-6. https://doi.org/10.1016/j.jphotobiol.2012.03.009

[37]

Stefanov, M., Rashkov, G., Borisova, P., & Apostolova, E. (2023). Sensitivity of the photosynthetic apparatus in maize and sorghum under different drought levels. Plants, 12(9), 1863. https://doi.org/10.3390/plants12091863

[38]

Agathokleous, E. (2021). The rise and fall of photosynthesis: Hormetic dose response in plants. Journal of Forestry Research, 32(2), 889-898. https://doi.org/10.1007/s11676-020-01252-1

[39]

Wiedman, S., & Appleby, A. (2006). Plant growth stimulation by sublethal concentrations of herbicides. Weed Research, 12(1), 65-74. https://doi.org/10.1111/j.1365-3180.1972.tb01188.x

[40]

Vukadinović, L., Galić, V., Brkić, A., Jambrović, A., & Šimić, D. (2025). Comparing chlorophyll fluorescence and hyperspectral indices in drought-stressed young plants in a maize diversity panel. Agronomy, 15(7), 1604. https://doi.org/10.3390/agronomy15071604

[41]

Mohamed, E., Tomimatsu, H., & Hikosaka, K. (2025). The relationships between photochemical reflectance index (PRI) and photosynthetic status in radish species differing in salinity tolerance. Journal of Plant Research, 138(2), 231-241. https://doi.org/10.1007/s10265-025-01615-x

[42]

Lee Jones, A., Ormondroyd, A., Hayes, F., & Jeffers, E. S. (2024). Reflections of stress: Ozone damage in broadleaf saplings can be identified from hyperspectral leaf reflectance. Environmental Pollution, 360, 124642. https://doi.org/10.1016/j.envpol.2024.124642

[43]

Manley, P. V., Sagan, V., Fritschi, F. B., & Burken, J. G. (2019). Remote sensing of explosives-induced stress in plants: Hyperspectral imaging analysis for remote detection of unexploded threats. Remote Sensing, 11(15), 1827. https://doi.org/10.3390/rs11151827

[44]

Clevers, J., & Kooistra, L. (2012). Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2), 574-583. https://doi.org/10.1109/jstars.2011.2176468

[45]

Tian, Y., Xie, L., Wu, M., Yang, B., Ishimwe, C., Ye, D., & Weng, H. (2021). Multicolor fluorescence imaging for the early detection of salt stress in Arabidopsis. Agronomy, 11(12), 2577. https://doi.org/10.3390/agronomy11122577

[46]

Murchie, E. H., & Ruban, A. V. (2020). Dynamic non-photochemical quenching in plants: From molecular mechanism to productivity. The Plant Journal, 101(4), 885-896. https://doi.org/10.1111/tpj.14601

[47]

Stefanov, M. A., Rashkov, G. D., & Apostolova, E. L. (2022). Assessment of the photosynthetic apparatus functions by chlorophyll fluorescence and P700 absorbance in C3 and C4 plants under physiological conditions and under salt stress. International Journal of Molecular Sciences, 23(7), 3768. https://doi.org/10.3390/ijms23073768

[48]

Didaran, F., Kordrostami, M., Ghasemi-Soloklui, A. A., Pashkovskiy, P., Kreslavski, V., Kuznetsov, V., & Allakhverdiev, S. I. (2024). The mechanisms of photoinhibition and repair in plants under high light conditions and interplay with abiotic stressors. Journal of Photochemistry and Photobiology B: Biology, 259, 113004. https://doi.org/10.1016/j.jphotobiol.2024.113004

[49]

Sharma, N., Nagar, S., Thakur, M., Suriyakumar, P., Kataria, S., Shanker, A. K., Landi, M., & Anand, A. (2023). Photosystems under high light stress: Throwing light on mechanism and adaptation. Photosynthetica, 61(2), 250-263. https://doi.org/10.32615/ps.2023.021

[50]

Neuwirthová, E., Lhotáková, Z., & Albrechtová, J. (2017). The effect of leaf stacking on leaf reflectance and vegetation indices measured by contact probe during the season. Sensors, 17(6), 1202. https://doi.org/10.3390/s17061202

[51]

Tian, Z., Fan, J., Yu, T., de Leon, N., Kaeppler, S. M., & Zhang, Z. (2025). Mitigating NDVI saturation in imagery of dense and healthy vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 227, 234-250. https://doi.org/10.1016/j.isprsjprs.2025.06.013

[52]

Gawlik-Dziki, U., Wrzesińska-Krupa, B., Nowak, R., Pietrzak, W., Zyprych-Walczak, J., & Obrępalska-Stęplowska, A. (2023). Herbicide resistance status impacts the profile of non-anthocyanin polyphenolics and some phytomedical properties of edible cornflower (Centaurea cyanus L.) flowers. Scientific Reports, 13(1), 11538. https://doi.org/10.1038/s41598-023-38520-z

[53]

Ruban, A. V. (2016). Nonphotochemical chlorophyll fluorescence quenching: Mechanism and effectiveness in protecting plants from photodamage. Plant Physiology, 170(4), 1903-1916. https://doi.org/10.1104/pp.15.01935

[54]

Baker, N., & Oxborough, K. (2004). Chlorophyll fluorescence as a probe of photosynthetic productivity. In G. C. Papageorgiou & Govindjee (Eds.), Chlorophyll a fluorescence. advances in photosynthesis and respiration (Vol. 19, pp. 65-82). Springer. https://doi.org/10.1007/978-1-4020-3218-9_3

[55]

Maxwell, K., & Johnson, G. N. (2000). Chlorophyll fluorescence—A practical guide. Journal of Experimental Botany, 51(345), 659-668. https://doi.org/10.1093/jexbot/51.345.659

[56]

Murchie, E. H., & Lawson, T. (2013). Chlorophyll fluorescence analysis: A guide to good practice and understanding some new applications. Journal of Experimental Botany, 64(13), 3983-3998. https://doi.org/10.1093/jxb/ert208

[57]

Jiao, Q., & Hu, X. (2025). Recent advances and emerging trends in chlorophyll fluorescence parameter Fv/Fm. Phyton-International Journal of Experimental Botany, 94(9), 2615-2630. https://doi.org/10.32604/phyton.2025.069246

[58]

Catteau, A., Le Guernic, A., Palos Ladeiro, M., Dedourge-Geffard, O., Bonnard, M., Bonnard, I., Delahaut, L., Bado-Nilles, A., Porcher, J. M., Lopes, C., Geffard, O., & Geffard, A. (2023). Integrative biomarker response-Threshold (IBR-T): Refinement of IBRv2 to consider the reference and threshold values of biomarkers. Journal of Environmental Management, 341, 118049. https://doi.org/10.1016/j.jenvman.2023.118049

[59]

Eceiza, M. V., Barco-Antoñanzas, M., Gil-Monreal, M., Huybrechts, M., Zabalza, A., Cuypers, A., & Royuela, M. (2022). Role of oxidative stress in the physiology of sensitive and resistant Amaranthus palmeri populations treated with herbicides inhibiting acetolactate synthase. Frontiers in Plant Science, 13, 1040456. https://doi.org/10.3389/fpls.2022.1040456

[60]

Kwon, C. S., & Penner, D. (1995). Response of a chlorsulfuron-resistant biotype of Kochia scoparia to ALS inhibiting herbicides and piperonyl butoxide. Weed Science, 43(4), 561-565. https://doi.org/10.1017/S0043174500081649

RIGHTS & PERMISSIONS

2026 The Author(s). New Plant Protection published by John Wiley & Sons Australia, Ltd on behalf of Institute of Plant Protection, Chinese Academy of Agricultural Sciences.

PDF (2011KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/