We propose a novel tree classification system called Treelogy, that fuses deep representations with hand-crafted features obtained from leaf images to perform leaf-based plant classification. Key to this system are segmentation of the leaf from an untextured background, using convolutional neural networks (CNNs) for learning deep representations, extracting hand-crafted features with a number of image processing techniques, training a linear SVM with feature vectors, merging SVM and CNN results, and identifying the species from a dataset of 57 trees. Our classification results show that fusion of deep representations with hand-crafted features leads to the highest accuracy. The proposed algorithm is embedded in a smart-phone application, which is publicly available. Furthermore, our novel dataset comprised of 5408 leaf images is also made public for use of other researchers.
It is worth noting that some studies developed mobile applications to serve their approaches through which users can benefit from the automated leaf classification algorithms. For example, LeafSnap @cite_20 which is developed for iOS, is one of the first released applications of this kind. Subsequently, Pl@ntNet @cite_13 mobile application, a more sophisticated one, has been released. A similar application MobileFlora @cite_26 , which is for flower recognition, is also relevant. We also built a mobile application to showcase our tree identification system. This application can be freely downloaded from Google Play Store https: play.google.com store apps details?id=com.payinekereg.treelogy .
@cite_26 @cite_13 @cite_20
2213241010 1969238582 1968896562 2529366711
We describe the first mobile app for identifying plant species using automatic visual recognition. The system --- called Leafsnap --- identifies tree species from photographs of their leaves. Key to this system are computer vision components for discarding non-leaf images, segmenting the leaf from an untextured background, extracting features representing the curvature of the leaf's contour over multiple scales, and identifying the species from a dataset of the 184 trees in the Northeastern United States. Our system obtains state-of-the-art performance on the real-world images from the new Leafsnap Dataset --- the largest of its kind. Throughout the paper, we document many of the practical steps needed to produce a computer vision system such as ours, which currently has nearly a million users. Pl@ntNet is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool. In this paper, we employ probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90 . Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation. This paper describes the participation of the Floristic consortium to the LifeCLEF 2016 plant identification challenge[18]. The aim of the task was to produce a list of relevant species for a large set of plant images related to 1000 species of trees, herbs and ferns living in Western Europe, knowing that some of these images belonged to unseen categories in the training set like plant species from other areas, horticultural plants or even off topic images (people, keyboards, animals, etc). To address this challenge, we first experimented as a baseline, without any rejection procedure, a Convolutional Neural Network (CNN) approach based on a slightly modified GoogLeNet model. In a second run, we applied a simple rejection criteria based on probability threshold estimation on the output of the CNN, one for each species, for removing automatically species propositions judged irrelevant. In the third run, rather than definitely eliminating some species predictions with the risk to remove false negative propositions, we applied various attenuation factors in order to revise the probability distributions given by the CNN as confident score expressing how much a query was related or not to the known species. More precisely, for this last run we used the geographical information and several cohesion measures in terms of observation, "organ" tags and taxonomy (genus and family levels) based on a knn similarity search results within the training set.