Advertisement

Environmental Science and Pollution Research

, Volume 26, Issue 2, pp 1848–1856 | Cite as

Prediction of cadmium concentration in brown rice before harvest by hyperspectral remote sensing

  • Weihong Zhou
  • Jingjing Zhang
  • Mengmeng Zou
  • Xiaoqing Liu
  • Xiaolong Du
  • Qian Wang
  • Yangyang Liu
  • Ying Liu
  • Jianlong LiEmail author
Research Article
  • 56 Downloads

Abstract

Cadmium (Cd) contaminated rice has become a global food security issue. Hyperspectral remote sensing can do rapid and nondestructive monitoring of environmental stress in plant. To realize the nondestructive detection of Cd in brown rice before harvest, the leaf spectral reflectance of rice exposed to six different levels of Cd stress was measured during the whole life stages. In addition, the dry weight of rice grain and Cd concentrations in brown rice were measured after harvest. The impact of Cd stress on the quantity and the quality of rice grain and on the leaf reflectance of rice was analyzed, and hyperspectral estimation models for predicting the Cd content in brown rice during three growth stages were established. The results showed that rice plants can impact the quality of the brown rice seriously, even if the impact on the quantity was not significant. All the established models had the capability to estimate Cd concentrations in brown rice (R2 > 0.598), and the best performance model, with the R2 value of 0.873, was use first derivative spectrum of booting stage as variable. It was concluded that the hyperspectral of rice leaves provides a new insight to predict Cd concentration in brown rice before harvest.

Keywords

Hyperspectral Brown rice Cd concentration Before harvest Booting stage Derivative transformation 

Notes

Acknowledgments

We are grateful to the editor and anonymous reviewers.

Funding information

This research was mainly supported by the “National key R & D project (No. 2018YFD0800201),” “Key Project of Chinese National Programs for Fundamental Research and Development (973 Program, No. 2010CB950702),” “APN Global Change Fund Project (No. ARCP2015-03CMY-Li),” and “Suzhou Science and Technology Project of China (SNG201447).”

References

  1. Allbed A, Kumar L, Sinha P (2014) Mapping and modelling spatial variation in soil salinity in the Al Hassa oasis based on remote sensing indicators and regression techniques. Remote Sens 6:1137–1157.  https://doi.org/10.1016/j.rse.2004.12.009 CrossRefGoogle Scholar
  2. And TK, Sommer S (2002) Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ Sci Technol 36:2742–2747CrossRefGoogle Scholar
  3. Arao T, Ishikawa S, Murakami M, Abe K, Maejima Y, Makino T (2010) Heavy metal contamination of agricultural soil and countermeasures in Japan. Paddy Water Environ 8:247–257CrossRefGoogle Scholar
  4. Bandaru V, Hansen DJ, Codling EE, Daughtry CS, White-Hansen S, Green CE (2010) Quantifying arsenic-induced morphological changes in spinach leaves: implications for remote sensing. Int J Remote Sens 31:4163–4177CrossRefGoogle Scholar
  5. Bandaru V, Daughtry CS, Codling EE, Hansen DJ, Whitehansen S, Green CE (2016) Evaluating leaf and canopy reflectance of stressed rice plants to monitor arsenic contamination. Int J Environ Res Public Health 13:606.  https://doi.org/10.3390/ijerph13060606 CrossRefGoogle Scholar
  6. Bao J, Shen Y, Jin L (2007) Determination of thermal and retrogradation properties of rice starch using near-infrared spectroscopy. J Cereal Sci 46:75–81CrossRefGoogle Scholar
  7. Cakmak I, Welch RM, Erenoglu B, Römheld V, Norvell WA, Kochian LV (2000) Influence of varied zinc supply on re-translocation of cadmium (109Cd) and rubidium (86Rb) applied on mature leaf of durum wheat seedlings. Plant Soil 219:279–284CrossRefGoogle Scholar
  8. Caporaso N, Whitworth MB, Fisk ID (2018) Near-infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains. Appl Spectrosc Rev 1–21Google Scholar
  9. Collins W (1988) Airborne biogeophysical mapping of hidden mineral deposits. Econ Geol 78:737–749CrossRefGoogle Scholar
  10. Cui YJ, Zhu YG, Zhai RH, Chen DY, Huang YZ, Qiu Y, Liang JZ (2004) Transfer of metals from soil to vegetables in an area near a smelter in Nanning, China. Environ Int 30:785–791CrossRefGoogle Scholar
  11. Elmasry G, Wang N, Elsayed A, Ngadi M (2007) Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J Food Eng 81:98–107CrossRefGoogle Scholar
  12. Geladi P, Kowalski BR (1985) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17CrossRefGoogle Scholar
  13. Geladi P, Burger J, Lestander T (2004) Hyperspectral imaging: calibration problems and solutions. Chemom Intell Lab Syst 72:209–217CrossRefGoogle Scholar
  14. Geladi PLM, Grahn HF, Burger JE (2007) Multivariate images, hyperspectral imaging: background and equipment. In: Grahn H, Geladi P (eds) Techniques and applications of hyperspectral image analysis. Wiley, Hoboken, pp 1–15Google Scholar
  15. Gomez C, Lagacherie P, Coulouma G (2008) Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma 148:141–148CrossRefGoogle Scholar
  16. Grant CA, Clarke JM, Duguid S, Chaney RL (2008) Selection and breeding of plant cultivars to minimize cadmium accumulation. Sci Total Environ 390:301–310CrossRefGoogle Scholar
  17. Gu YW, Li S, Gao W, Wei H (2015) Hyperspectral estimation of the cadmium content in leaves of Brassica rapa chinesis based on the spectral parameters. Acta Ecol Sin 35:4445–4453 (in Chinese)CrossRefGoogle Scholar
  18. Horler DNH, Dockray M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote Sens 4:273–288CrossRefGoogle Scholar
  19. Huang J et al (2015) Integrating hierarchical bioavailability and population distribution into potential eco-risk assessment of heavy metals in road dust: a case study in Xiandao District, Changsha city, China. Sci Total Environ 541:969–976CrossRefGoogle Scholar
  20. Hui W et al (2017) Wheat canopy spectral reflectance feature response to heavy metal copper and zinc stress. Trans Chin Soc Agric Eng 33:171–176 (in Chinese)Google Scholar
  21. Ji CK, Nejad ZD, Jung MC (2017) Arsenic and heavy metals in paddy soil and polished rice contaminated by mining activities in Korea. Catena 148:92–100CrossRefGoogle Scholar
  22. Jmr A, Hoo Fung LA, Grant CN, Dennis HT, Lalor GC (2012) Dietary intake of minerals and trace elements in rice on the Jamaican market. J Food Compos Anal 26:111–121CrossRefGoogle Scholar
  23. Kashiwagi T, Shindoh K, Hirotsu N, Ishimaru K (2009) Evidence for separate translocation pathways in determining cadmium accumulation in grain and aerial plant parts in rice. BMC Plant Biol 9:8.  https://doi.org/10.1186/1471-2229-9-8 CrossRefGoogle Scholar
  24. Kawamura S (1999) Determination of undried rough rice constituent content using near-infrared transmission spectroscopy. Trans ASAE 42:813–818CrossRefGoogle Scholar
  25. Kelly RW, Phinn SR, Strong WM, Lester, Butler D, Robson APL (2006) Predicting grain protein content in wheat using hyperspectral sensing of in-season crop canopies and partial least square regression. Int J Geoinform 2:93–108Google Scholar
  26. Knyazikhin Y, Schull MA, Stenberg P, Mottus M, Rautiainen M, Yang Y, Marshak A, Latorre Carmona P, Kaufmann RK, Lewis P, Disney MI, Vanderbilt V, Davis AB, Baret F, Jacquemoud S, Lyapustin A, Myneni RB (2013) Hyperspectral remote sensing of foliar nitrogen content. Pnas 110:E185–E192CrossRefGoogle Scholar
  27. Kooistra L, Wanders J, Epema GF, Leuven RSEW, Wehrens R, Buydens LMC (2003) The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Anal Chim Acta 484:189–200CrossRefGoogle Scholar
  28. Kumagai M, Ohisa N, Amano T, Ogawa N (2003) Canonical discriminant analysis of cadmium content levels in unpolished rice using a portable near-infrared spectrometer. Anal Sci 19:1553–1555.  https://doi.org/10.2116/analsci.19.1553 CrossRefGoogle Scholar
  29. Li H, Luo N, Li YW, Cai QY, Li HY, Mo CH, Wong MH (2017) Cadmium in rice: transport mechanisms, influencing factors, and minimizing measures. Environ Pollut 224:622–630CrossRefGoogle Scholar
  30. Lim HS, Lee JS, Chon HT, Sager M (2008) Heavy metal contamination and health risk assessment in the vicinity of the abandoned Songcheon Au–Ag mine in Korea. J Geochem Explor 96:223–230CrossRefGoogle Scholar
  31. Liu YL, Hui C, Wu GF, Wu XG (2010) Feasibility of estimating heavy metal concentrations in Phragmites australis using laboratory-based hyperspectral data - a case study along Le'an River, China. Int J Appl Earth Obser Geoinfor 12:S166–S170CrossRefGoogle Scholar
  32. Liu M, Liu X, Ding W, Wu L (2011) Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis. Int J Appl Earth Obs Geoinf 13:246–255CrossRefGoogle Scholar
  33. Liu M, Liu X, Li J, Li T (2012) Estimating regional heavy metal concentrations in rice by scaling up a field-scale heavy metal assessment model. Int J Appl Earth Obs Geoinf 19:12–23CrossRefGoogle Scholar
  34. Lv J, Liu X (2011) Predicting arsenic concentration in rice plants from hyperspectral data using random forests. In: Jin DLS (ed) Advances in multimedia, software engineering and computing Vol.1. Advances in intelligent and soft computing, vol 128. Springer, Berlin Heidelberg, pp 601–606CrossRefGoogle Scholar
  35. Meharg AA, Norton G, Deacon C, Williams P, Adomako EE, Price A, Zhu Y, Li G, Zhao FJ, McGrath S, Villada A, Sommella A, de Silva PMCS, Brammer H, Dasgupta T, Islam MR (2013) Variation in rice cadmium related to human exposure. Environ Sci Technol 47:5613–5618CrossRefGoogle Scholar
  36. Niu L, Yang F, Xu C, Yang H, Liu W (2013) Status of metal accumulation in farmland soils across China: from distribution to risk assessment. Environ Pollut 176:55–62CrossRefGoogle Scholar
  37. Oman SD (1984) Multivariate calibration. In: Chemometrics. Springer, Netherlands, pp 61–93Google Scholar
  38. Ouzounidou G, Moustakas M, Eleftheriou EP (1997) Physiological and ultrastructural effects of cadmium on wheat ( Triticum aestivum L.) leaves. Arch Environ Contam Toxicol 32:154–160CrossRefGoogle Scholar
  39. PRC MoEPo, PRC MoLaRo (2014) National survey of soil pollution. http://www.zhb.gov.cn/gkml/hbb/qt/201404/t20140417_270670.htm. Accessed 17 Apr 2014
  40. Pruvot C, Douay F, Hervé F, Waterlot C (2006) Heavy metals in soil, crops and grass as a source of human exposure in the former mining areas (6 pp). J Soils Sediments 6:215–220CrossRefGoogle Scholar
  41. Qin S, Shuang H, Li Z, Chen S, Xu S (2015) The metal element information extraction from hyperion data based on the vegetation stress spectra. Earth Sci 40:1319–1324 (in Chinese)Google Scholar
  42. Rascio N, Vecchia FD, Ferretti M, Merlo L, Ghisi R (1993) Some effects of cadmium on maize plants. Arch Environ Contam Toxicol 25:244–249CrossRefGoogle Scholar
  43. Rodda MS, Li G, Reid RJ (2011) The timing of grain Cd accumulation in rice plants: the relative importance of remobilisation within the plant and root Cd uptake post-flowering. Plant Soil 347:105–114CrossRefGoogle Scholar
  44. Shen F, Wu Q, Shao X, Zhang Q (2018) Non-destructive and rapid evaluation of aflatoxins in brown rice by using near-infrared and mid-infrared spectroscopic techniques. J Food Sci Technol 55:1–10CrossRefGoogle Scholar
  45. Shi T, Chen Y, Liu Y, Wu G (2014) Visible and near-infrared reflectance spectroscopy-an alternative for monitoring soil contamination by heavy metals. J Hazard Mater 265:166–176CrossRefGoogle Scholar
  46. Stobart AK, Griffiths WT, Ameen-Bukhari I, Sherwood RP (1985) The effect of Cd2+ on biosynthesis of chlorophyll in leaves of barley. Physiol Plant Physiologia Plantarum 63:293–298CrossRefGoogle Scholar
  47. Thorp KR, Wang G, Bronson KF, Badaruddin M, Mon J (2017) Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield. Comput Electron Agric 136:1–12CrossRefGoogle Scholar
  48. Uraguchi S, Fujiwara T (2012) Cadmium transport and tolerance in rice: perspectives for reducing grain cadmium accumulation. Rice 5:5.  https://doi.org/10.1186/1939-8433-5-5 CrossRefGoogle Scholar
  49. Uraguchi S, Mori S, Kuramata M, Kawasaki A, Arao T, Ishikawa S (2009) Root-to-shoot Cd translocation via the xylem is the major process determining shoot and grain cadmium accumulation in rice. J Exp Bot 60:2677–2688.  https://doi.org/10.1093/jxb/erp119 CrossRefGoogle Scholar
  50. Wang F, Gao J, Zha Y (2018) Hyperspectral sensing of heavy metals in soil and vegetation: feasibility and challenges. ISPRS J Photogramm Remote Sens 136:73–84CrossRefGoogle Scholar
  51. Wu QT, Chen L, Wang GS (1999) Differences on Cd uptake and accumulation among rice cultivars and its mechanism. Acta Ecol Sin 19:104–107 (in Chinese)Google Scholar
  52. Xia YS, Jiang-Hua HE (2004) Analysis of the status of farm produce pollution in Guangdong province. Ecol Environ 13:109–111 (in Chinese)Google Scholar
  53. Xiao X, Boles S, Liu J, Zhuang D, Frolking S, Li C, Salas W, Moore B III (2005) Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens Environ 95:480–492.  https://doi.org/10.1016/j.rse.2004.12.009 CrossRefGoogle Scholar
  54. Xie LH, Tang SQ, Wei XJ, Shao GN, Jiao GA, Sheng ZH, Luo J, Hu PS (2017) The cadmium and lead content of the grain produced by leading Chinese rice cultivars. Food Chem 217:217–224CrossRefGoogle Scholar
  55. Yoneyama T, Gosho T, Kato M, Goto S, Hayashi H (2010) Xylem and phloem transport of Cd, Zn and Fe into the grains of rice plants (Oryza sativa L.) grown in continuously flooded Cd-contaminated soil. Soil Sci Plant Nutr 56:445–453CrossRefGoogle Scholar
  56. Yu H, Wang J, Fang W, Yuan J, Yang Z (2006) Cadmium accumulation in different rice cultivars and screening for pollution-safe cultivars of rice. Sci Total Environ 370:302–309CrossRefGoogle Scholar
  57. Zhang TT, Zeng SL, Gao Y, Ouyang ZT, Li B, Fang CM, Zhao B (2011) Using hyperspectral vegetation indices as a proxy to monitor soil salinity. Ecol Indic 11:1552–1562CrossRefGoogle Scholar
  58. Zhang X, Zhong T, Liu L, Ouyang X (2015) Impact of soil heavy metal pollution on food safety in China. PLoS One 10:e0135182CrossRefGoogle Scholar
  59. Zhao D, Reddy KR, Kakani VG, Read JJ, Carter GA (2003) Corn ( Zea mays L.) growth, leaf pigment concentration, photosynthesis and leaf hyperspectral reflectance properties as affected by nitrogen supply. Plant Soil 257:205–218CrossRefGoogle Scholar
  60. Zhao K, Valle D, Popescu S, Zhang X, Mallick B (2013) Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sens Environ 132:102–119CrossRefGoogle Scholar
  61. Zhu XR, Gao-Yang LI, Dong-Lin SU, Liu W, Shan Y, Branch L (2015) The feasibility of rapid determination of the cadmium content in rice based on near infrared spectroscopy and synergy interval partial least squares. Food & Machinery 31:43–46 50 (in Chinese)Google Scholar
  62. Zhuang P, Zhang C, Li Y, Zou B, Mo H, Wu K, Wu J, Li Z (2016) Assessment of influences of cooking on cadmium and arsenic bioaccessibility in rice, using an in vitro physiologically-based extraction test. Food Chem 213:206–214CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Life SciencesNanjing UniversityNanjingPeople’s Republic of China
  2. 2.Suzhou Institute of TechnologyJiangsu University of Science and TechnologyZhangjiagangChina

Personalised recommendations