Evaluation of different methods of determining the Canopy Cover of Corn

Articles in Press

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

Monitoring and evaluating the impact of farm management on plant growth is of great importance. One of the indicators that showing the condition of the plant during its growth period is the Canopy Cover(CC). The aim of this study was to investigate the accuracy of two methods of determining the CC of corn during different stages of growth in the field. For this purpose, imaging was performed at different intervals throughout the corn growing season in four modes: top-down without a fisheye lens, top-down with a fisheye lens, bottom-up without a fisheye lens, and bottom-up with a fisheye lens. The amount of CC in the resulting images was determined by three algorithms: maximum likelihood, minimum distance, and the parallel method in ENVI and the Canopeo software. Statistical evaluation of the results from ENVI indicated a high accuracy of the model using the maximum likelihood algorithm (Kappa coefficient > 0.82, overall accuracy > 93%, minimum Commission and Omission errors). Comparison of the results from ENVI and Canopeo with the results from the AquaCrop plant model indicated that the CC obtained from bottom-up imaging without a lens processed by Canopeo (CC = 0.8, RMSE = 11.81) and the CC obtained from top-down imaging with a lens processed by ENVI (CC = 0.82, RMSE = 13.26) were more effective in determining CC compared to other modes.

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Iranian Journal of Soil and Water Research

Articles in Press, Accepted Manuscript
Available Online from 24 November 2024

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