A guided multi-scale categorization of plant species in natural images

Automatic categorization of plant species in natural images is an important computer vision problem with numerous applications in agriculture and botany. The problem is particularly challenging due to the large number of plant species, the inter-species similarity, the large scale variations in natural images, and the lack of annotated data. In this paper, we present a guided multi-scale approach that segments the regions of interest (containing a plant) from a complex background of the natural image and systematically extracts scale-representative patches based on those regions. These multi-scale patches are used to train state-of-the-art Convolutional Neural Network (CNN) models that analyze a given plant image and determine its species. Focusing specifically on the identification of plant species in natural images, we show that the proposed approach is a very effective way of making deep learning models more robust to scale variations. We perform a comprehensive experimental evaluation of our proposed method over several CNN models. Our best result on the Inception-ResNet-v2 model achieves a top-1 classification accuracy of 89.21% for 100 plant species which represents a 5.4% increase over using random cropping to generate training data.