Floristic participation at LifeCLEF 2016 Plant Identification Task

International audienceThis 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

A comparative study of fine-grained classification methods in the context of the LifeCLEF plant identification challenge 2015

International audienceThis paper describes the participation of Inria to the plant identification task of the LifeCLEF 2015 challenge. The aim of the task was to produce a list of relevant species for a large set of plant observations related to 1000 species of trees, herbs and ferns living in Western Europe. Each plant observation contained several annotated pictures with organ/view tags: Flower, Leaf, Fruit, Stem, Branch, Entire, Scan (exclusively of leaf). To address this challenge, we experimented two popular families of classification techniques, i.e. convolutional neural networks (CNN) on one side and fisher vectors-based discriminant models on the other side. Our results show that the CNN approach achieves much better performance than the fisher vectors. Beyond, we show that the fusion of both techniques, based on a Bayesian inference using the confusion matrix of each classifier, did not improve the results of the CNN alone

Advanced shape context for plant species identification using leaf image retrieval

International audienceThis paper presents a novel method for leaf species identification combining local and shape-based features. Our approach extends the shape context model in two ways. First of all, two different sets of points are distinguished when computing the shape contexts: the voting set, i.e. the points used to describe the coarse arrangement of the shape and the computing set containing the points where the shape contexts are computed. This representation is enriched by introducing local features computed in the neighborhood of the computing points. Experiments show the effectiveness of our approach

RECURSIVE PATH FOLLOWING IN LOG POLAR SPACE FOR AUTONOMOUS LEAF CONTOUR EXTRACTION

Use of image segmentation has caused agriculture advancement in species identification, chlorophyll measurements, plant growth and disease detection. Most methods require some level of manual segmentation as autonomous image segmentation is a difficult task. Methods with the highest segmentation precision use a priori knowledge obtained from user input which is time consuming and subjective. This research focuses on providing current segmentation methods a pre-processing model that autonomously extracts an internal and external contour of the leaf. The model converts the uniform Cartesian images to non-uniformly sampled images in log polar space. A recursive path following algorithm was designed to map out the leaf’s edge boundary. This boundary is shifted inward and outward to create two contours; one that lies within the foreground and one within the background. The image database consists of 918 leaves from multiple plants and different background mediums. The model successfully created contours for 714 of the leaves. Results of the autonomously created contours being used in lieu of user-input contours for a current segmentation algorithm are presented

Understanding Leaves in Natural Images - A Model-Based Approach for Tree Species Identification

International audienceWith the aim of elaborating a mobile application, accessible to anyone and with educational purposes, we present a method for tree species identification that relies on dedicated algorithms and explicit botany-inspired descriptors. Focusing on the analysis of leaves, we developed a working process to help recognize species, starting from a picture of a leaf in a complex natural background. A two-step active contour segmentation algorithm based on a polygonal leaf model processes the image to retrieve the contour of the leaf. Features we use afterwards are high-level geometrical descriptors that make a semantic interpretation possible, and prove to achieve better performance than more generic and statistical shape descriptors alone. We present the results, both in terms of segmentation and classification, considering a database of 50 European broad-leaved tree species, and an implementation of the system is available in the iPhone application Folia

Assessing the Effects of the Smartphone as a Learning Tool on the Academic Achievement and Motivation of High School Agriculture Students in Louisiana

Perhaps the most influential device in modern society is the smartphone. Over 90% of Americans aged 18-29 own a smartphone and 74% of teenagers reported using a smartphone as their primary internet connection. Students perceived that using smartphones in the classroom aided learning. However, two-thirds of American high schools ban students from using phones in the classroom. Secondary science curriculum focuses on subjects that regard the biodiversity of plant and animal species, but disregard the student’s ability to identify species. Consequently, secondary students in general are very poor at identifying species of trees. Previous research supports the idea that advanced smartphone applications in student centered learning environments can improve achievement and motivation. There is little in the agricultural education literature pertaining to smartphone enhanced learning among secondary agriculture students. Further, no research has focused on the use of smartphone applications in forestry education at the secondary level. This dual-purpose study compared achievement levels between two groups of students in a forestry curriculum learning with smartphones or printed materials and determined motivational differences between groups. Specifically, one group of students used the smartphone apps Leafsnap, V-Tree, Tree Book, and Quizlet to identify leaf samples while a comparison group utilized Leaf Key to Common Trees of Louisiana (Dozier & Mills, 2005), Important Forest Trees of the Eastern United States (Brockman & Merrilees, 1991), and Louisiana Trees (Hodges, Evans & Garnett, 2015). A non-equivalent comparison group design was employed. Secondary agricultural students (n = 263) from 13 schools across Louisiana completed a criterion referenced pretest and post-test created by the researcher via Test Generator Web©. Motivation was measured using the Course Interest Survey (Keller, 2010). Data were analyzed using Hierarchical Linear Modelling (HLM) for fixed effects with maximum likelihood estimation to determine if any statistically significant differences existed between the groups in achievement or motivation. HLM accounted for differences between individual students in schools and prior knowledge. The analysis rendered no statistically significant differences between the groups in achievement or motivation. It was concluded that smartphones do not reduce learning and should be considered a learning enabler in agricultural education where policy permits

A Parametric Active Polygon for Leaf Segmentation and Shape Estimation

International audienceThe identication of tree leaves from photographs taken in a natural environment is a di cult task where the quality of the seg- mentation is of primary importance. In this paper we present a system that combines a rst, unre ned segmentation step, with an estimation of descriptors depicting the general shape of a simple leaf. It is based on a light polygonal model, built to represent most of the leaf shapes, that will be deformed to t the leaf in the image. Avoiding some classic obstacles of active contour models, this approach gives promising results, even on complex natural photographs, and constitutes a solid basis for a leaf recognition process