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A Stacking-Based Ensemble Learning Method for Available Nitrogen Soil Prediction with a Handheld Micronear-Infrared Spectrometer

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Journal of Applied Spectroscopy Aims and scope

Soil-available nitrogen is a vital index related to the growth and development of crops. The real-time and nondestructive detection of the soil-available nitrogen content based on near-infrared (NIR) spectroscopy could improve the accurate management of crop nutrients. In this manuscript, soil NIR spectroscopy and available nitrogen data are used in a stacked framework to develop a reliable and accurate soil-available nitrogen model. The spectral reflectance of the soil samples was collected in the 900 to 1700 nm band with nine pre-processing methods using a handheld micronear-infrared spectrometer. The stacking framework of this manuscript has two layers. Extreme gradient boosting (XGBoost), categorical boosting (CatBoost), a light gradient boosting machine (LightGBM), and a random forest, which are tree-based algorithms, are stacked as base models in the first layer. In the second layer, linear regression is employed in a meta-model to identify the unique learning pattern of the base model. The results show that the range and characteristics of the spectra can be used to make relevant predictions , and the micro-NIR spectra are variable under different pre-treatments. In addition, the stacked model achieves the best performance of all the models tested. Notably, the coefficient of determination (R2) is 0.942, and the relative percent difference is 4.192 with Savitzky–Golay and multiplicative scatter correction. This manuscript presents an efficient method for predicting soil-available nitrogen levels with a handheld micronear-infrared spectrometer.

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References

  1. X. Mu and Y. Chen, Plant Physiol. Biochem., 158, 76–82 (2021).

    Article  Google Scholar 

  2. X. Wang, J. Fan, Y. Xing, G. Xu, H. Wang, J. Deng, Y. Wang, F. Zhang, P. Li, and Z. Li, Adv. Agronomy, 153, 121–173 (2019).

    Article  Google Scholar 

  3. T. Terhoeven-Urselmans, H. Schmidt, R. G. Joergensen, and B. Ludwig, Soil Biol. Biochem., 40, No. 5, 1178–1188 (2008).

    Article  Google Scholar 

  4. R. V. Rossel and R. Webster, Eur. J. Soil Sci., 63, No. 6, 848–860 (2012).

    Article  Google Scholar 

  5. R. V. Rossel, S. R. Cattle, A. Ortega, and Y. Fouad, Geoderma, 150, Nos. 3–4, 253–266 (2009).

    Article  ADS  Google Scholar 

  6. B. Stenberg, R. A. V. Rossel, A. M. Mouazen, and J. Wetterlind, Adv. Agronomy, 107, 163–215 (2010).

    Article  Google Scholar 

  7. S. Nawar and A. M. Mouazen, Sensors, 17, No. 10, Article ID 2428 (2017).

  8. T. Leng, F. Li, Y. Chen, L. Tang, J. Xie, and Q. Yu, Meat Sci., 180, Article ID 108559 (2021).

  9. M. Knadel, L. W. de Jonge, M. Tuller, H. U. Rehman, P. W. Jensen, P. Moldrup, M. H. Greve, and E. Arthur, Vadose Zone J., 19, No.1, Article ID e20007 (2020).

  10. V. Ulissi, F. Antonucci, P. Benincasa, M. Farneselli, G. Tosti, M. Guiducci, F. Tei, C. Costa, F. Pallottino, and L. Pari, Sensors, 11, No. 6, 6411–6424 (2011).

    Article  ADS  Google Scholar 

  11. Y. Shao and Y. He, Soil Res., 49, No. 2, 166–172 (2011).

    Article  Google Scholar 

  12. J. Tang, J. Liang, C. Han, Z. Li, and H. Huang, Accid. Anal. Prev., 122, 226–238 (2019).

    Article  Google Scholar 

  13. H.-C. Yi, Z.-H. You, M.-N. Wang, Z.-H. Guo, Y.-B. Wang, and J.-R. Zhou, BMC Bioinformatics, 21, No. 1, 1–10 (2020).

    Article  Google Scholar 

  14. F. Liu, R. Zhao, and L. Shi, arXiv preprint arXiv, 2103, Article ID 13124 (2021).

  15. Å. Rinnan, F. V. D. Berg, and S. B. Engelsen, TrAC Trends Anal. Chem., 28, No. 10, 1201–1222 (2009).

    Article  Google Scholar 

  16. A. Savitzky and M. J. Golay, Anal. Chem., 36, No. 8, 1627–1639 (1964).

    Article  ADS  Google Scholar 

  17. S. Nawar, H. Buddenbaum, J. Hill, J. Kozak, and A. M. Mouazen, Soil Till. Res., 155, 510–522 (2016).

    Article  Google Scholar 

  18. R. J. Barnes, M. S. Dhanoa, and S. J. Lister, Appl. Spectrosc., 43, No. 5, 772–777 (1989).

    Article  ADS  Google Scholar 

  19. T. Isaksson and T. Næs, Appl. Spectrosc., 42, No. 7, 1273–1284 (1988).

    Article  ADS  Google Scholar 

  20. A. Peirs, A. Schenk, and B. M. Nicolaı̈, Postharvest Biol. Technol., 35, No. 1, 1–13 (2005).

  21. M. S. Askari, J. Cui, S. M. O'Rourke, and N. M. Holden, Soil Till. Res., 146, 108–117 (2015).

    Article  Google Scholar 

  22. H. Liang, M. Zhang, C. Gao, and Y. Zhao, Sensors, 18, No. 6, Article ID 1963 (2018).

  23. X. Jin, L. Wang, W. Zheng, X. Zhang, L. Liu, S. Li, Y. Rao, and J. Xuan, Measurement, Article ID 110553 (2021).

  24. A. Gholizadeh, L. Borůvka, M. M. Saberioon, J. Kozak, R. Vašát, and K. Němeček, Soil Water Res., 10, No. 4, 218–227 (2015).

    Article  Google Scholar 

  25. Y. Sun, M. Yuan, X. Liu, M. Su, L. Wang, Y. Zeng, H. Zang, and L. Nie, Microchem. J., 159, Article ID 105492 (2020).

  26. J. Duckworth, Near-Infrared Spectrosc. Agric., 44, 113–132 (2004).

    Google Scholar 

  27. M. S. Dhanoa, S. J. Lister, R. Sanderson, and R. J. Barnes, J. Near Infrared Spectrosc., 2, No. 1, 43–47 (1994).

    Article  ADS  Google Scholar 

  28. B. M. Nicolai, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, Postharvest Biol. Technol., 46, No. 2, 99–118 (2007).

    Article  Google Scholar 

  29. A. Wagner, S. Hilgert, T. Kattenborn, and S. Fuchs, Water Supply, 19, No. 4, 1204–1211 (2019).

    Article  Google Scholar 

  30. S. Katuwal, M. Knadel, T. Norgaard, P. Moldrup, M. H. Greve, and L. W. de Jonge, Geoderma, 361, Article ID 114080 (2020).

  31. J.-H. Cheng and D.-W. Sun, Food Eng. Rev., 9, No. 1, 36–49 (2017).

    Article  Google Scholar 

  32. M. H. D. M. Ribeiro and L. dos Santos Coelho, Appl. Soft Computing, 86, Article ID 105837 (2020).

  33. X. Luo, L. Xu, P. Huang, Y. Wang, J. Liu, Y. Hu, P. Wang, and Z. Kang, Agriculture, 11, No. 7, 673 (2021).

    Article  Google Scholar 

  34. T. Chen and C. Guestrin, Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 785–794 (2016).

  35. L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, arXiv preprint arXiv, 1706, Article ID 09516 (2017).

  36. G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, Adv. Neural Information Proc. Systems, 30, 3146–3154 (2017).

    Google Scholar 

  37. L. Breiman, Machine Learning, 45, No. 1, 5–32 (2001).

    Article  Google Scholar 

  38. Y. Li, Y. Lei, P. Wang, M. Jiang, and Y. Liu, Appl. Soft Computing, 101, Article ID 107003 (2021).

  39. T. Wu, W. Zhang, X. Jiao, W. Guo, and Y. Alhaj Hamoud, Computers Electron. Agric., 184 (2021).

  40. E. Al Daoud, Int. J. Computer Information Eng., 13, No. 1, 6–10 (2019).

  41. R. Zornoza, C. Guerrero, J. Mataix-Solera, K. Scow, V. Arcenegui, and J. Mataix-Beneyto, Soil Biol. Biochem., 40, No. 7, 1923–1930 (2008).

    Article  Google Scholar 

  42. L. C. Lee, C.-Y. Liong, ad A. A. Jemain, AIP Conference Proceedings (AIP Publishing LLC, 020116 (2018).

  43. S. Chen, H. Xu, D. Xu, W. Ji, S. Li, M. Yang, B. Hu, Y. Zhou, N. Wang, and D. Arrouays, Geoderma, 400, Article ID 115159 (2021).

  44. C. H. Bazoni, E. I. Ida, D. F. Barbin, and L. E. Kurozawa, J. Stored Prod. Res., 73, 1–6 (2017).

    Article  Google Scholar 

  45. Y. Hong, S. Chen, Y. Liu, Y. Zhang, L. Yu, Y. Chen, Y. Liu, H. Cheng, and Y. Liu, Catena, 174, 104–116 (2019).

    Article  Google Scholar 

  46. W. Ni, L. Nørgaard and M. Mørup, Anal. Chim. Acta, 813, 1–14 (2014).

    Article  Google Scholar 

  47. A. Kartakoullis, J. Comaposada, A. Cruz-Carrión, X. Serra, and P. Gou, Food Chem., 278, 314–321 (2019).

    Article  Google Scholar 

  48. W. Ji, R. Viscarra Rossel, and Z. Shi, Eur. J. Soil Sci., 66, No. 3, 555–565 (2015).

  49. E. W. Ciurczak, Pract. Spectrosc. Ser., 27, 7–18 (2001).

    Google Scholar 

  50. M. Haest, T. Cudahy, C. Laukamp, and S. Gregory, Economic Geology, 107, No. 2, 209–228 (2012).

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agriculture University, Hefei, China

    Midi Wan, Xiu Jin, Yalu Han, Lianglong Wang, Shaowen Li, Yuan Rao, Xiaodan Zhang & Qijuan Gao

  2. College of Information and Computer Science at Anhui Agricultural University, Hefei, China

    Midi Wan, Xiu Jin, Yalu Han, Lianglong Wang, Shaowen Li, Yuan Rao, Xiaodan Zhang & Qijuan Gao

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  1. Midi Wan
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  2. Xiu Jin
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  3. Yalu Han
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  4. Lianglong Wang
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  6. Yuan Rao
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  7. Xiaodan Zhang
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Correspondence to Xiu Jin.

Additional information

Abstract of article is published in Zhurnal Prikladnoi Spektroskopii, Vol. 89, No. 6, p. 907, November–December, 2022.

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Wan, M., Jin, X., Han, Y. et al. A Stacking-Based Ensemble Learning Method for Available Nitrogen Soil Prediction with a Handheld Micronear-Infrared Spectrometer. J Appl Spectrosc 89, 1241–1253 (2023). https://doi.org/10.1007/s10812-023-01491-0

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