https://www.selleckchem.com/pr....oducts/17-AAG(Geldan
However, the CNN models require a large amount of labelled samples for the training process. We conducted another experiment based on training with crop images at mature stages and testing at early stages. The k-FLBPCM method outperformed the state-of-the-art CNN models in recognizing small leaf shapes at early growth stages, with error rates an order of magnitude lower than CNN models for canola-radish (crop-weed) discrimination using a subset extracted from the "bccr-segset" dataset, and for the "mixed-plants" dataset. Mo
Like
Comment
Share
