Multispectral detection of floral buds for automated thinning of pear

The recorded multispectral images were converted to a database containing the spatial-spectral signatures of the objects present in the orchard. Subsequently, canonical correlation analysis was applied to create a spectral discriminant model that detects pixels originating from floral buds. This model was then applied to the recorded data after which an image analysis algorithm was designed and optimized to predict the number of floral buds. In total, approximately 87% of the visible floral buds were detected correctly with a low false discovery rate (<16%). Therefore, it is expected that the multispectral sensor can be used to improve the efficiency of existing thinning devices. Additionally, it could as well be used as a stand-alone sensor for early-season yield estimation.