FUSION OF THREE OPTICAL SENSORS FOR NONDESTRUCTIVE DETECTION OF WATER CONTENT IN LETTUCE CANOPIES
Abstract
Experiments were conducted to develop and assess a method by which the water content of a lettuce canopy can be nondestructively detected and estimated using a combination of spectra, RGB images, and canopy temperature. To this end, 130 lettuce samples grown in four different substrate water content levels were collected for data acquisition. In the spectroscopy procedure, five spectral intervals (380 variables) were selected by backward interval partial least squares and were further reduced to 48 wavelength variables, chosen using a genetic algorithm based on Savitzky–Golay smoothing and log (1/R) transformation. Then, 967, 1170, 1221, 1406, 1484, 1942, and 1985 nm optimum spectral variables were selected by the successive projection algorithm. Thirteen plant features were extracted from top- and front-view RGB images. These features comprised morphological, color, and textural features. An empirical crop water stress index was established based on dry and wet reference surfaces via thermal imagery. Subsequently, a principal component analysis was applied to the spectral variables and the image features, and an extreme learning machine was used to construct the multisensor and single-sensor models. The results show that the multisensor model had a correlation coefficient of prediction of 0.9018, which was found to be approximately 9.4 and 15.7% better than that of the spectral and image models. This work demonstrates that integrating spectra, RGB images, and canopy temperature with suitable algorithms offers a high potential for use in the nondestructive measurement of water content in lettuce, considerably improving accuracy over that using a single-sensor modality.
Keywords
spectrum,
RGB images,
canopy temperature,
multi-sensors data fusion,
nondestructive detection of water content
Supporting agencies: This study was funded by the Key Technologies Research and Development Program of China (grant number 2017YFD0700504); the Program of the National Natural Science Foundation of China (grant number 61771224); the Natural Science Foundation of Jiangsu Province of China (grant number BK20180864); the Postdoctoral Research Foundation of China (grant number 2017M621650); Synergistic Innovation Center of Jiangsu Modern Agricultural Equipment and Technology (grant number 4091600028); Open Fund of the Ministry of Education Key Laboratory of Modern Agricultural Equipment and Technology (grant number JNZ201903); Project of Faculty of Agricultural Equipment of Jiangsu University (grant number NZXB20200203).
About the Authors
H. Y. Gao
School of Agricultural Engineering at Jiangsu University
China
Zhenjiang 212013
China
Zhenjiang 212013
H. P. Mao
School of Agricultural Engineering at Jiangsu University
China
Zhenjiang 212013
China
Zhenjiang 212013
X. D. Zhang
School of Agricultural Engineering at Jiangsu University
China
Zhenjiang 212013
China
Zhenjiang 212013
I. Ullah
School of Agricultural Engineering at Jiangsu University
China
Zhenjiang 212013
China
Zhenjiang 212013
X. H. Wei
School of Agricultural Engineering at Jiangsu University
China
Zhenjiang 212013
China
Zhenjiang 212013
References
1. Ü. Kızıl, L. Genç, M. İ. Nalpulat, D. Ş. Apolyo, M. Mı̇rı̇k, Zemdirbyste, 99, 409–418 (2012).
2. A. M. H. Elmetwalli, A. N. Tyler, P. D. Hunter, C. A. Salt, Remote Sens. Lett., 3, 363–372 (2012).
3. M. El-Bially, H. Saudy, I. El-Metwally, M. Shahin, Agric. Water Manage., 208, 132–139 (2018).
4. N. Sanchez, A. Gonzalez-Zamora, J. Martinez-Fernandez, M. Piles, M. Pablos, Agric. Meteorol., 259, 141–153 (2018).
5. F. M. Danson, M. D. Steven, T. J. Malthus, J. A. Clark, Int. J. Remote Sens., 13, 461–470 (1992).
6. J. G. P. W. Clevers, L. Kooistra, M. E. Schaepman, Int. J. Appl. Earth Obs. Geoinform., 12, 119–125 (2010).
7. Y. Hendrawan, H. Murase, Expert Syst. Appl., 38, 14321–14335 (2011).
8. S. Zhuang, P. Wang, B. Jiang, M. S. Li, Z. H. Gong, Comput. Electron. Agric., 140, 461–468 (2017).
9. B. A. King, K. C. Shellie, Comput. Electron. Agric., 145, 122–129 (2018).
10. E. G. Kullberg, K. C. DeJonge, J. L. Chavez, Agric. Water Manage., 179, 64–73 (2017).
11. O. Adeyemi, I. Grove, S. Peets, Y. Domun, T. Norton, Comput. Electron. Agric., 153, 102–114 (2018).
12. H. Y. Gao, H. P. Mao, X. D. Zhang, Zemdirbyste, 102, 51–58 (2015).
13. H. P. Mao, H. Y. Gao, X. D. Zhang, F. Kumi, Sci. Hortic., 184, 1–7 (2015).
14. A. Khorsandi, A. Hemmat, S. A. Mireei, R. Amirfattahi, P. Ehsanzadeh, Agric. Water Manage., 204, 222–233 (2018).
15. H. G. Jones, Application of Thermal Imaging and Infrared Sensing in Plant Physiology and Ecophysiology, Elsevier, London, 107–163 (2004).
16. P. R. Petrie, Y. N. Wang, S. Liu, S. Lam, M. A. Whitty, M. A. Skewes, Biosyst. Eng., 179, 126–139 (2019).
17. S. B. Idso, R. D. Jackson, P. J. Pinter Jr., R. J. Reginato, J. L. Hatfield, Agric. Meteorol., 24, 45–55 (1981).
18. P. Ceccato, S. Flasse, S. Tarantola, S. Jacquemoud, J. M. Gregoire, Remote Sens. Environ., 77, 22–33 (2001).
19. Y. Özyı̇ğit, M. Bı̇Lgen, J. Agric. Sci. Technol., 15, 1537–1545 (2013).
20. A. Savitzky, M. J. E. Golay, Anal. Chem., 36, 1627–1639 (1964).
21. T. Shi, Y. Chen, H. Liu, J. Wang, G. Wu, Appl. Spectrosc., 68, 831–837 (2014).
22. R. K. H. Galvão, M. C. U. Araujo, G. E. José, M. J. C. Pontes, E. C. Silva, T. C. B. Saldanha, Talanta, 67, 736–740 (2005).
23. R. Leardi, L. Nørgaard, J. Chemometr., 18, 486–497 (2004).
24. R. Leardi, J. Chemometr., 14, 643–655 (2010).
25. M. C. U. Araujo, T. C. B. Saldanha, R. K. H. Galvao, T. Yoneyama, H. C. Chame, V. Visani, Chemometr. Intell. Lab. Syst., 57, 65–73 (2001).
26. D. Story, M. Kacira, C. Kubota, A. Akoglu, L. An, Comput. Electron. Agric., 74, 238–243 (2010).
27. T. Hang, N. Lu, M. Takagaki, H. P. Mao, Sci. Hortic., 252, 113–120 (2019).
28. F. Bachofer, G. Queneherve, T. Zwiener, M. Maerker, V. Hochschild, Eur. J. Remote Sens., 49, 205–224 (2016).
29. N. Agam, Y. Cohen, J. A. J. Berni, V. Alchanatis, D. Kool, A. Dag, U. Yermiyahu, A. Ben-Gal, Agric. Water Manage., 118, 79–86 (2013).
30. F. R. S. Karl Pea, J. Sci., 2, 559–572 (1901).
31. G. B. Huang, D. H. Wang, Y. Lan, Int. J. Mach. Learn. Cybern., 2, 107–122 (2011).
32. S. Ullah, A. K. Skidmore, A. Ramoelo, T. A. Groen, M. Naeem, A. Ali, ISPRS-J. Photogramm. Remote Sens., 93, 56–64 (2014).
33. I. Torres, M. T. Sanchez, M. Benlloch-Gonzalez, D. Perez-Marin, Biosyst. Eng., 180, 50–58 (2019).
34. D. L. Mangus, A. Sharda, N. Q. Zhang, Comput. Electron. Agric., 121, 149–159 (2016).
35. W. H. Maes, K. Steppe, J. Exp. Bot., 63, 4671–4712 (2012).
36. Z. Thungo, H. Shimelis, A. O. Odindo, J. Mashilo, Acta Agric. Scand. Sect. B-Soil Plant Sci., 70, 177–194 (2020).
37. P. J. Zarco-Tejada, V. Gonzalez-Dugo, L. E. Williams, L. Suarez, J. A. J. Berni, D. Goldhamer, E. Fereres, Remote Sens. Environ., 138, 38–50 (2013).
38. S. Chung, L. E. Breshears, J.-Y. Yoon, Comput. Electron. Agric., 154, 93–98 (2018).
Review
For citations:
Gao H.Y., Mao H.P., Zhang X.D., Ullah I., Wei X.H. FUSION OF THREE OPTICAL SENSORS FOR NONDESTRUCTIVE DETECTION OF WATER CONTENT IN LETTUCE CANOPIES. Zhurnal Prikladnoii Spektroskopii. 2021;88(1):170(1)-170(9).
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