High-Resolution Flowering Index for Canola Yield Modelling

Abstract

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

Unveiling the Invasion: Advancing Ecological Mapping of Heterotheca Subaxillaris Through Integrated Remote Sensing Techniques With Drones and Satellites

“…These results align well with Dixon et al [13], who detected eucalyptus from the UAV image mosaic. Various studies have used drones to prepare ground truth of flowering with high accuracy, including work on barley [37], almonds [34], canola [59], as well as invasive species [9,23]. Using drone-derived ground truths for training and validating the outcomes of the multi-scale models that detect the local and short-term ecological events from satellite images shows the opportunities offered by combining different sensors with different strengths for ecosystem monitoring [13].…”