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In this paper, we investigate the use of deep learning approaches for plant classification (cauliflower and weeds) in smart agriculture applications. To perform this, five approaches were considered, two based on well-known deep learning architectures (AlexNet and GoogleNet), and three based on Support Vector Machine (SVM) classifiers with different feature sets (Bag of Words in L*a*b colour space, Bag of Words in HSV colour space, Bag of Words of Speeded-up Robust Features (SURF)). Two types of datasets were used in this study: one without Data Augmentation and the second one with Data Augmentation. Each algorithm's performance was tested with one data set similar to the training data, and a second data set acquired under challenging conditions such as various weather conditions, heavy weeds, and several weed species that have a similarity of colour and shape to the crops. Results show that the best overall performance was achieved by DL-based approaches.