A large training sample is prerequisite for the successful training of each deep learning model for image classification. Collecting a large dataset is time-consuming and costly, especially for plants. When a large dataset is not available, the challenge is how to use a small or medium size dataset to train a deep model optimally. To overcome this challenge, a novel model is proposed to use the available small size plant dataset efficiently. This model focuses on data augmentation and aims to improve the learning accuracy by oversampling the dataset through representative image patches. To extract the relevant patches, ORB key points are detected in the training images and then image patches are extracted using an innovative algorithm. The extracted ORB image patches are used for dataset augmentation to avoid overfitting during the training phase. The proposed model is implemented using convolutional neural layers, where its structure is based on ResNet architecture. The proposed model is evaluated on a challenging ACHENY dataset. ACHENY is a Chenopodiaceae plant dataset, comprising 27030 images from 30 classes. The experimental results show that the patch-based strategy outperforms the classification accuracy achieved by traditional deep models by 9%.
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