Abstract:The use of machine learning and computer vision methods for recognizing different plants from images has attracted lots of attention from the community. This paper aims at comparing local feature descriptors and bags of visual words with different classifiers to deep convolutional neural networks (CNNs) on three plant datasets; AgrilPlant, LeafSnap, and Folio. To achieve this, we study the use of both scratch and fine-tuned versions of the GoogleNet and the AlexNet architectures and compare them to a local feature descriptor with k-nearest neighbors and the bag of visual words with the histogram of oriented gradients combined with either support vector machines and multi-layer perceptrons. The results shows that the deep CNN methods outperform the hand-crafted features. The CNN techniques can also learn well on a relatively small dataset, Folio.

Data augmentation plays a crucial role in increasing the number of training images, which often aids to improve classification performances of deep learning techniques for computer vision problems. In this paper, we employ the deep learning framework and determine the effects of several data-augmentation (DA) techniques for plant classification problems. For this, we use two convolutional neural network (CNN) architectures, AlexNet and GoogleNet trained from scratch or using pretrained weights. These CNN models are then trained and tested on both original and data-augmented image datasets for three plant classification problems: Folio, AgrilPlant, and the Swedish leaf dataset. We evaluate the utility of six individual DA techniques (rotation, blur, contrast, scaling, illumination, and projective transformation) and several combinations of these techniques, resulting in a total of 12 data-augmentation methods. The results show that the CNN methods with particular dataaugmented datasets yield the highest accuracies, which also surpass previous results on the three datasets. Furthermore, the CNN models trained from scratch profit a lot from data augmentation, whereas the fine-tuned CNN models do not really profit from data augmentation. Finally, we observed that data-augmentation using combinations of rotation and different illuminations or different contrasts helped most for getting high performances with the scratch CNN models.

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Abstract-Most research in image classification has focused on applications such as face, object, scene and character recognition. This paper examines a comparative study between deep convolutional neural networks (CNNs) and bag of visual words (BOW) variants for recognizing animals. We developed two variants of the bag of visual words (BOW and HOG-BOW) and examine the use of gray and color information as well as different spatial pooling approaches. We combined the final feature vectors extracted from these BOW variants with a regularized L2 support vector machine (L2-SVM) to distinguish between classes within our datasets. We modified existing deep CNN architectures: AlexNet and GoogleNet, by reducing the number of neurons in each layer of the fully connected layers and last inception layer for both scratch and pre-trained versions. Finally, we compared the existing CNN methods, our modified CNN architectures and the proposed BOW variants on our novel wild-animal dataset (Wild-Anim). The results show that the CNN methods significantly outperform the BOW techniques.

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