High-Resolution Flowering Index for Canola Yield Modelling
نویسندگان
چکیده
Canola (Brassica napus), with its prominent yellow flowers, has unique spectral characteristics and necessitates special indices to quantify the flowers. This study investigated four for high-resolution RGB images segmenting flower pixels. The compared vegetation digitally canola area develop a seed yield prediction model. A small plot (2.75 m × 6 m) experiment was conducted at Kernen Research Farm, Saskatoon, where grown under six row spacings eight seeding rates replicates (192 plots). canopy reflectance imaged using (0.15 cm ground sampling distance) 100 MP iXU 1000 sensor mounted on an unpiloted aerial vehicle (UAV). were evaluated their efficiency in identifying pixels linear discriminant analysis (LDA). Digitized pixel used as predictor of models. Seventy percent data model training 30% testing. Models performance metrics: coefficient determination (R2) root mean squared error (RMSE). High-resolution Flowering Index (HrFI), new index proposed this study, identified most accurate detecting pixels, especially imagery containing within-canopy shadow There strong, positive associations between digitized peak flowering timing having greater R2 (0.82) early (0.72). Cumulative predicted 75% yield. Our results indicate that HrFI Modified Yellowness (MYI) better predictors NDYI RBNI (Red Blue Normalizing Index) they able discriminate petals shadows. We suggest further studies evaluate MYI medium-resolution UAV satellite imagery.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14184464