Evaluation of different methods of determining the Canopy Cover of Silage Maize

Document Type : Research Paper

Authors

1 Water Sci. & Eng. Dept., Faculty of agriculture and natural Res., Imam Khomeini International University, Qazvin, Iran.

2 Staff of Irrigation and reclamation Dept., Faculty of agriculture and natural resources, University of Tehran, Karaj, Iran

Abstract

Vegetation indices effectively represent plant conditions in the field. Since Canopy Cover (CC) correlates with the plant’s photosynthetic activity, this study aimed to evaluate the accuracy of two methods for determining CC in silage maize during different growth stages in a maize field in Qazvin using ENVI software and the Canopeo model and to compare the results with values obtained from the AquaCrop model. Imaging was conducted at different time intervals throughout the maize growing season in four scenarios: 1) top-down without a fisheye lens, 2) top-down with a fisheye lens, 3) bottom-up without a fisheye lens, and 4) bottom-up with a fisheye lens. The CC values in the obtained images were determined using three algorithms: maximum likelihood, minimum distance, and parallel method in ENVI. Initially, a qualitative assessment of image classification was performed using the three mentioned algorithms. The results indicated that the maximum likelihood algorithm had higher accuracy compared to the other two algorithms. The Statistical evaluation of the quantitative results from ENVI demonstrated high model accuracy in the maximum likelihood algorithm (Kappa coefficient > 0.82, overall accuracy > 93%, and minimal Commission and Omission errors). The lowest RMSE value was observed for CC estimated using the Canopeo software with bottom-up imaging with a lens (9.92). In general, it was found that bottom-up imaging without a lens (Canopeo) (R=0.8 and RMSE=11.81) and top-down imaging with a lens (ENVI) (R=0.82 and RMSE=13.26) were more capable in determining CC than the other Scenarios.

Keywords

Main Subjects

EXTENDED ABSTRACT

 

Introduction:

Increasing population, climate change, and reduced access to water resources have intensified the need to increase yield per unit area more than ever. Generally, crop yield is influenced by factors such as the environment, management practices, and genotype. Therefore, evaluating plant conditions in the field to achieve maximum yield is of great importance. Canopy cover, which is directly related to the amount of photosynthesis in the plant, is one of the important indices in the AquaCrop plant model for determining yield and management conditions in the field. Despite AquaCrop's sensitivity to CC, the default values of this index were often considered when running the model.

Purpose:

Given the importance of determining CC and the high sensitivity of the AquaCrop model, the aim of this study was to evaluate the accuracy of CC estimation by the two models, ENVI and Canopeo.

Research method:

Four imaging modes of vegetation cover were considered during the growth of corn. The imaging treatments included: 1) top-down imaging with a fisheye lens, 2) top-down imaging without a fisheye lens, 3) bottom-up imaging with a fisheye lens, and 4) bottom-up imaging without a fisheye lens. it should be pointed out that the nadir images were captured from 9 AM to 12 PM. To select the best image classification method for determining CC using ENVI, three methods were used: maximum likelihood, minimum distance, and parallel. Then the appropriate method was selected by examining the Kappa coefficient, overall accuracy, Commission, and Omission. The AquaCrop model was run under field conditions, and the standard CC value was obtained from the model. Finally, the CC obtained from the three models, AquaCrop, ENVI, and Canopeo, was evaluated using the statistical indices correlation coefficient (CC) and RMSE.

Results:

At first, the three classification algorithms in ENVI were evaluated. In the maximum likelihood method, the Kappa coefficient ranged from 0.82 to 0.97, overall accuracy ranged from 93.69 to 99.14, Commission error ranged from 17.07 to 59.44, and Omission error ranged from 4 to 19.63, indicating that its performance was better than other classification methods. The qualitative evaluation of the results from the Canopeo model also indicated sufficient accuracy in estimating CC. Comparing the results from ENVI and Canopeo with AquaCrop showed that the RMSE had the minimum value in the Canopeo model for bottom-up imaging with a fisheye lens (RMSE = 9.92). The correlation coefficient of the results from the ENVI and Canopeo models with AquaCrop was satisfactory (0.68 to 0.97). Overall, the CC determined by Canopeo for bottom-up imaging without a lens(CC=0.8, RMSE= 11.81) and the CC determined by the maximum likelihood algorithm in ENVI for top-down imaging with a fisheye lens(CC= 0.82, RMSE= 13.26) showed the best performance.

Conclusion:

The accuracy of CC estimation by Canopeo model was higher than that of ENVI ; Also, the determination of CC in Canopeo required less time than ENVI. In the images recorded from the top-bottom with a fish eye lens, the error in CC determination increased due to the increase in the number of objects and distortion. The impact of the mentioned errors were greater in the determination of CC by Canopeo.

Author Contributions

 Conceptualization, Abbas.Kaviani., Zahra.Partovi.; methodology, Abbas.Kaviani., Zahra.Partovi. and Hadi.Ramezani.Etedali.; software, Zahra.Partovi.; validation, Abbas.Kaviani., Zahra.Partovi.;formal analysis, Abbas.Kaviani., Zahra.Partovi.;investigation, Abbas. Kaviani., Zahra.Partovi.; resources, Zahra.Partovi.; data curation, Abbas. Kaviani., Zahra.Partovi., Hadi.Ramezani.Etedali., Masoud.Soltani., Leila. Khosravi.; writing—original draft preparation, Zahra.Partovi.; writing—review and editing, Abbas. Kaviani.; visualization, Zahra.Partovi.; supervision, Abbas. Kaviani., Hadi.ramezani.Etedali.; project administration, Abbas. Kaviani.; funding acquisition, Abbas. Kaviani., Leila Khosravi., All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Due to the nature of the research, due to [ethical/ legal/ commercial] supporting data is not available.

Acknowledgements

The authors feel it necessary to express their gratitude to Hezar Jolfa Agro-Industrial Company and the Iranian Space Research Institute for their sincere cooperation in advancing the present research.

Ethical considerations

The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest.

References

Adabi, V., Azizian, G, A., Ramezani, E H., Kaviyani, A., Adabi, B. (2020). Local sensitivity analysis of AquaCrop model for Wheat and Maize in Qazvin plain and Moghan Pars_Abad in Iran. Iranian Journal of Irrigation and Drainage. 13. 1565-1579. (In Persian).
Bruinsma, J. (2017). World agriculture: Towards 2015/2030: an FAO study. 1-431. https://doi.org/10.4324/9781315083858.
Erenstein, O., Jalet, M., Sonder, K., Mottaleb, K., Prasanna, B, M. (2022). Global maize production, consumption and trade: trends and R&D implications. Food Security. 14. 1295-1319. https://doi.org/10.1007/s12571-022-01288-7.
FAO. (2012). Reference Manual, Chapter 3 – AquaCrop, Version 4.0.
Garcia, M, H., Flores, M, H., Ascencio, H, R., Khalil, G, A., Tijerina, C, L., Mancilla, V, O. R., Vazquez, P, M, A. (2020). Corn grain yield estimation from vegetation indices, canopy cover, plant density, and a neural network using multispectral and RGB images acquired with unmanned aerial vehicles. Agriculture.10. 1-24.
Goodwin, A. W., Lindsey, L. E., Harrison, S. K., & Paul, P. A. (2018). Estimating Wheat Yield with Normalized Difference Vegetation Index and Fractional Green Canopy Cover. Crop, Forage & Turfgrass Management, 4. 1–6.
Govindasamy, P., Mahawer, S. K., Sarangi, D., Halli, H. M., Das, T, K., Raj, R., Pooniya, V., Muralikrishnan, L., Kumar, S., Chandra, A. (2022). The Comparison of Canopeo and SamplePoint for Measurement of Green Canopy Cover for Forage Crops in India. MethodsX,
Hedayati, D, A., & Kakavand, R. (2012). Climatic zoning of Qazvin province. NIVAR, 36(77). (In Persian)
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
Jorenush, M. H., Nasab, S. B., Taghvaeian, S., Pakparvar, M., & Sherbafi, A. (2022). Evaluation of wheat canopy cover using NDVI in large areas of Iran. Iran Agricultural Research. 41.1–8.
Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote Sensing and Image Interpretation. John Wiley & Sons.
Lu, Y., Chibarabada, T. P., Ziliani, M. G., Onema, J. M. K., McCabe, M. F., Sheffield, J. (2021). Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model. Agricultural Water Management, 252. https://doi.org/10.1016/j.agwat.2021.106884
Luiz, J., De Souza, M., Tamiris De Oliveira, C., Lais, S., Rosa, K., Tsukahara, R. Y. (2022). Sensitivity analysis of AquaCrop model for maize crop in a humid subtropical climate in Brazil. CIGR Journal. 24.
McHugh, M. (2012). Interrater_reliability_The_kappa_statistic. Biochemia Mwdica, 22(3), 276–282.
Nielsen, D. C., Miceli-Garcia, J. J., & Lyon, D. J. (2012). Canopy cover and leaf area index relationships for wheat, triticale, and corn. Agronomy Journal, 104(6), 1569–1573.
Patrignani, A., & Ochsner, T. E. (2015). Canopeo: A powerful new tool for measuring fractional green canopy cover. Agronomy Journal, 107(6), 2312–2320.
Pearson, K. (1997). Mathematical Contributions to the Theory of Evolution.–On a Form of Spurious Correlation Which May Arise When Indices Are Used in the Measurement of Organs. Proceedings of the Royal Society of London, 60, 489–498.
Saedi, R., Ramezani E, H., Sotoodeh N, A., Nazari, B., & Kaviyani, A. (2021). Evaluation of the AquaCrop model in estimating the trend of changes in soil moisture, evaporation-transpiration and corn yield under salinity and fertility stresses. Environmental Stresses in Crop Sciences. (In Persian)
Shan, Y., Li, G., Su, L., Zhang, J., Wang, Q., Wu, J., Mu, W., & Sun, Y. (2022). Performance of AquaCrop Model for Maize Growth Simulation under Different Soil Conditioners in Shandong Coastal Area, China. Agronomy, 12(7), 1541.
Sodari, L., Alam, J., & Das, K. K. (2021). Performance Assessment of Aquacrop Model for Estimating Canopy Cover, Aboveground Biomass and Grain Yield of Winter Wheat in Saptari District: An Application to Irrigation Management. Proceedings of 9th IOE Graduate Conference.
Vanha, M, I., Salemaa, M., Tuominen, S., & Mikkola, K. (2000). Digitized photographs in vegetation analysis ‐ a comparison of cover estimates. Applied Vegetation Science, 3(1), 89–94.
Wang, G., Mehmood, F., Zain, M., Hamani, A. K. M., Xue, J., Gao, Y., & Duan, A. (2022). AquaCrop Model Evaluation for Winter Wheat under Different Irrigation Management Strategies: A Case Study on the North China Plain. Agronomy, 12(12), 3184.
Xing, H., XU, X., LI, Z., Chen, Y., Feng, H., Yang, G., & Chen, Z. (2017). Global sensitivity analysis of the AquaCrop model for winter wheat under different water treatments based on the extended Fourier amplitude sensitivity test. Journal of Integrative Agriculture, 16(11), 2444–2458. https://doi.org/10.1016/S2095-3119(16)61626-X
Iranian Journal of Soil and Water Research
Volume 55, Issue 12
March 2025
Pages 2467-2482

Files

Share

How to cite

Statistics