Comparison of Support Vector Machines and Deep Learning for Plant Classification in Smart Agriculture Applications Authors Esmael Hamuda 1, Ashkan Parsi 2, Martin Glavin 2 and Edward Jones 2, 1 Elmergib University, Libya, 2 University of Galway, Ireland Abstract 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 ...
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