Assessment of UAV based Canopy Cover for Varying Irrigation Treatments using Random Forest Classifier and MSAVI Index Thresholding

Canopy cover (CC) reflects canopy density, leaf area development, and early stress conditions acts as a significant indicator for crop health. Accurate CC estimation helps in mapping spatial variability in crops and facilitates early detection of disease or stress due to nutrient or water deficiency. For estimation of canopy cover, UAV multispectral data was acquired at different crop growth stages. This study estimated wheat canopy cover percentage from tillering to dough stage using Random forest classifier and MSAVI index thresholding for more accurate and robust assessment of canopy dynamics. Supervised classification approach was used based on given training samples for three different classes, i.e., soil, canopy and shadow and classification was performed through Random forest (RF) algorithm. The extracted canopy pixels were then used for finding canopy cover percentage. Additionally, a simplified approach was used based on MSAVI index thresholds to identify crop pixels, enabling reliable CC estimation through vegetation index segmentation method. The experiment was conducted on wheat crop using three ETc (Crop evapotranspiration) based irrigation treatments i.e., 100%, 80%, and 60% ETc and each treatment had three replications. In addition to ETc based treatments, the Farmer’s and rainfed treatments were also considered. The rainfed treatments with two replications, received a single life-saving irrigation and farmer treatments, with three replications were irrigated based on local farmer’s practice.

Canopy cover percentage observed across different growth stages (40 to 114 DAS) showed distinct variation in crop development among varying irrigation treatments. In the treatments with 100%, 80%, and 60% ETc irrigation, RF based CC ranged from 35.3–98.5%, 36.1–97.9%, and 29.2–95.2%, while MSAVI-based CC ranged from 33.8–96.5%, 34.2–95.9%, and 28.1–94.5%, respectively. In comparison to ETc treatments, farmers treatment exhibited lower canopy cover, with ranges of 28.6–95.6% (RF) and 28.9–92.8% (MSAVI). Rainfed treatment recorded the lowest CC values across the growing season, varying between 23.1–72.4% using RF and 25.3–69.7% using MSAVI. Canopy cover estimates from the Random Forest algorithm and the MSAVI index showed consistent seasonal patterns, with RF generally producing slightly higher CC values. The NDVI patterns were also observed for all stages to validate these findings and the values ranged from 0.29–0.89, 0.26–0.88, and 0.24–0.85 in 100%, 80%, and 60% ETc treatments, respectively. Rainfed (0.22–0.74) and Farmer’s treatments (0.26–0.81) had lower NDVI values, supported the CC trends observed with RF and MSAVI methods. The highest CC and NDVI values were obtained around flowering stage i.e., (85-95) DAS and the lowest at tillering stages for all treatments, followed by a gradual decline after the flowering stage as the crop progressed toward maturity. Canopy cover trends were comparable in the 100% and 80% ETc treatments, whereas CC in 60% ETc treatment remained lower at all stages, indicating the impact of water deficit on canopy growth.

The study highlights that MSAVI based vegetation-index methods can provide a reliable and highly efficient pathway for estimating canopy cover, reducing the need for extensive training datasets and complex classification models.