نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه علوم و مهدسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین المللی امام خمینی (ره)، قزوین، ایران
2 گروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین‌المللی امام خمینی (ره)، قزوین، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
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. 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 selected for comparison 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 were evaluated. 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 highlights 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.
کلیدواژه‌ها [English]
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