Hailstorms are a frequent natural weather disaster in the Canadian Prairies that can cause catastrophic damage to field crops. Assessment of damage for insurance claims requires insurance inspectors to visit individual fields and estimate damage on individual plants. This study computes temporal profiles and estimates the severity of hail damage to crops in 54 fields through the temporal analysis of vegetation indices calculated from Sentinel-2 images. The damage estimation accuracy of eight vegetative indices in different temporal analyses of delta index (pre-and post-hail differences) or area under curve (AUC) index (time profiles of index affected by hail) was compared. Hail damage was accurately quantified by using the AUC of 32 days of Normalized Difference Vegetation Indices (NDVI), Normalized Difference Water Index (NDWI), and Plant Senescence Radiation Index (PSRI). These metrics were well correlated with ground estimates of hail damage in canola (r = −0.90, RMSE = 8.24), wheat (r = −0.86, RMSE = 12.27), and lentil (r = 0.80, RMSE = 17.41). Thus, the time-series changes in vegetation indices had a good correlation with ground estimates of hail damage which may allow for more accurate assessment of the extent and severity of hail damage to crop land.

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Advances in high‐throughput platforms such as UAVs (unoccupied aerial vehicles) facilitate rapid image‐based phenotypic data acquisition. However, existing plot‐level data extraction methods are unreliable if field plots differ in size and spacing, as often occurs in early‐generation plant breeding trials. To overcome the limitations of conventional plot extraction techniques, a combinational approach with both field‐map information and image classification techniques can be used to optimize plot extraction. The objective of this study was to develop a plot boundary extraction workflow for irregularly sized and spaced field plots from UAV imagery using plot spacing data and vegetation index‐based classifiers. An herbicide screening experiment consisting of three replications of 780 lentil (Lens culinaris Medik.) populations was foliar sprayed with saflufenacil. Aerial image acquisition was conducted during the peak vegetation stage using a RedEdge multispectral camera. A semi‐automatic workflow was compiled in eCognition software to extract lentil plot boundaries. Normalized difference vegetation index (NDVI) was calculated to locate the plots with vegetation and those with low NDVI or no vegetation, and pixel resizing based on plot size and orientation was used to draw the plot boundary. The extraction results showed a precise estimation of plot boundary for all the plots with a wide range of herbicide damage, including the plots with complete loss of vegetation. By using a simple convolutional filter (line filter), image thresholding, and pixel resizing, this approach avoided the use of complex algorithm‐based methodologies. Results suggest that this workflow can be extended to a wide range of phenotyping studies.

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.

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