Canola Yield Estimation Using Remotely Sensed Images and M5P Model Tree Algorithm

Northwestern Ontario has a shorter growing season but fertile soil, affordable land, opportunities for agricultural diversification, and a demand for canola production. Canola yield mainly varies with spatial heterogeneity of soil properties, crop parameters, and meteorological conditions; thus, existing yield estimation models must be revised before being adopted in Northwestern Ontario to ensure accuracy. Region-specific canola cultivation guidelines are essential. This study utilized high spatial-resolution images to estimate flower coverage and yield in experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada. Spectral profiles were created for canola flowers and pods. During the peak flowering period, the reflectance of green and red bands was almost identical, allowing for the successful classification of yellow flower coverage using a recursive partitioning and regression tree algorithm. A notable decrease in reflectance in the RedEdge and NIR bands was observed during the transition from pod maturation to senescence, reflecting physiological changes. Canola yield was estimated using selected vegetation indices derived from images, the percent cover of flowers, and the M5P Model Tree algorithm. Field samples were used to calibrate and validate prediction models. The model’s prediction accuracy was high, with a correlation coefficient (r) of 0.78 and a mean squared error of 7.2 kg/ha compared to field samples. In conclusion, this study provided an important insight into canola growth using remote sensing. In the future, when modelling, it is recommended to consider other variables (soil nutrients and climate) that might affect crop development.