University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 55 7 2024 09 22 Assessment of canopy cover fraction in sugar beet field using unmanned aerial vehicle imagery and different image segmentation methods Assessment of canopy cover fraction in sugar beet field using unmanned aerial vehicle imagery and different image segmentation methods 1199 1215 98875 10.22059/ijswr.2024.371136.669647 FA Seyed Reza Haddadi Department of Water Sci. and Eng., Faculty of Agricultural and Natural Resources, Imam Khomeini International University, Qazvin, Iran. Masoud Soltani Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran Journal Article 2024 01 15 Canopy cover fraction is one of the most important criteria for investigating the crop growth and yield and is one of the input data of most plant models. Canopy cover fraction is an easier measurement than the other methods which id depended on field observations or image processing beyond the visible ‎spectrum. ‎ In this study, drone images of the sugar beet field in the cropping season of 2015-2016 and on the four dates from late May to late June at the Lindau center of plant sciences research, Switzerland were used. The research was conducted by six plant discrimination indices and three distinct thresholding algorithms to ‎segment sugar beet vegetation. ‎ Then, among the 18 investigated methods, the best 6 methods were evaluated by comparing their values with the ground truth values in 30 different regions of the farm and on four dates from the beginning of the four-leaf stage to the end of the six-leaf stage. Results showed that the ExG, GLI, and ‎RGBVI indices, in combination with the Otsu and Ridler-Calvard thresholding algorithms, ‎demonstrate optimal performance in vegetation segmentation. ‎ The evaluation statistics of NRMSE and R2 for the ExG&Otsu method as the most accurate method ‎were obtained as 5.13 % and 0.96, respectively.‎ Conversely, the RGBVI&RC method exhibits the least accuracy in the initial evaluation, with ‎NRMSE and R2 values of 8.18 % and 0.87, respectively. Comparative analysis of statistical indicators showed that the ExG&Otsu and ExG&RC methods with similar performance, displaying ‎the highest correlation with ground truths. Additionally, the GLI&Otsu method consistently demonstrates the lowest ‎error compared to ‎ground truths. ‎ Canopy cover fraction is one of the most important criteria for investigating the crop growth and yield and is one of the input data of most plant models. Canopy cover fraction is an easier measurement than the other methods which id depended on field observations or image processing beyond the visible ‎spectrum. ‎ In this study, drone images of the sugar beet field in the cropping season of 2015-2016 and on the four dates from late May to late June at the Lindau center of plant sciences research, Switzerland were used. The research was conducted by six plant discrimination indices and three distinct thresholding algorithms to ‎segment sugar beet vegetation. ‎ Then, among the 18 investigated methods, the best 6 methods were evaluated by comparing their values with the ground truth values in 30 different regions of the farm and on four dates from the beginning of the four-leaf stage to the end of the six-leaf stage. Results showed that the ExG, GLI, and ‎RGBVI indices, in combination with the Otsu and Ridler-Calvard thresholding algorithms, ‎demonstrate optimal performance in vegetation segmentation. ‎ The evaluation statistics of NRMSE and R2 for the ExG&Otsu method as the most accurate method ‎were obtained as 5.13 % and 0.96, respectively.‎ Conversely, the RGBVI&RC method exhibits the least accuracy in the initial evaluation, with ‎NRMSE and R2 values of 8.18 % and 0.87, respectively. Comparative analysis of statistical indicators showed that the ExG&Otsu and ExG&RC methods with similar performance, displaying ‎the highest correlation with ground truths. Additionally, the GLI&Otsu method consistently demonstrates the lowest ‎error compared to ‎ground truths. ‎ https://ijswr.ut.ac.ir/article_98875_673bba91f6cc3cf9b7a40e734660023a.pdf