Combining Leaf Salient Points and Leaf Contour Descriptions for Plant Species Recognition

International audienceManual Plant identification done by experts is tedious and time consuming. This process needs to be automatic and easy to handle by the different stakeholders. In this paper, we propose an original method for plant species recognition, based on the leaf observation. We consider two sources of information: the leaf margin and the leaf salient points. For the leaf shape description, we investigate the shape context descriptor and two multiscale triangular approaches: the well-known triangle area representation (TAR) and the triangle side length representation (TSL). We propose then their combination with a shape-context based descriptor that represents the spatial correlation between the leaf salient points and the leaf margin. Experiments are carried out on three public leaf datasets. Results show that our approach achieves a high retrieval accuracy and outperforms state-of-art methods

An Android Application for Leaf-based Plant Identification

International audienceThis paper presents an Android application for plant identification. The system relies on the observation of leaf images. Unlike other mobile plant identification applications, the user may choose the leaf characters that will guide the identification process. For this purpose, two kinds of descriptors are proposed to the user: a shape descriptor based on a multiscale triangular representation of the leaf margin and a descriptor of the salient points of the leaf. The application achieves good identification accuracy and provides Android users a useful system for plant identification

Inria's participation at ImageCLEF 2013 Plant Identification Task

International audienceThis paper describes the participation of Inria within the Pl@ntNet project at ImageCLEF2013 plant identification task. For the SheetAsBackground category (scans or photographs of leaves with a uniform background), the submitted runs used a multiscale triangle-based approaches, either alone or combined with other shape-based descriptors. For the NaturalBackground category (unconstrained photographs of leaves, flowers, fruits, stems,...), the four submitted runs used local features extracted using different geometric constraints. Three of them were based on large scale matching of individual local feature, while the last one used a Fisher vector representation. Metadata like the flowering date or/and plant identifier were successfully combined to the visual content. Overall the proposed methods performed very well for all categories and sub-categories

A shape-based approach for leaf classification using multiscale triangular representation

International audienceIn this paper we introduce a new multiscale shape-based approach for leaf image retrieval. The leaf is represented by local descriptors associated with margin sample points. Within this local description, we study four multiscale triangle representations: the well known triangle area representation (TAR), the triangle side lengths representation (TSL) and two new representations that we denote triangle oriented angles (TOA) and triangle side lengths and angle representation (TSLA). Unlike existing TAR approaches, where a global matching is performed, the similarity measure is based on a locality sensitive hashing of local descriptors. The proposed approach is invariant under translation, rotation and scale and robust under partial occlusion. Evaluations made on four public leaf datasets show that our shape-based approach achieves a high retrieval accuracy w.r.t. state-of-art methods

An expert botanical feature extraction technique based on phenetic features for identifying plant species

In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost

Computer Vision Problems in 3D Plant Phenotyping

In recent years, there has been significant progress in Computer Vision based plant phenotyping (quantitative analysis of biological properties of plants) technologies. Traditional methods of plant phenotyping are destructive, manual and error prone. Due to non-invasiveness and non-contact properties as well as increased accuracy, imaging techniques are becoming state-of-the-art in plant phenotyping. Among several parameters of plant phenotyping, growth analysis is very important for biological inference. Automating the growth analysis can result in accelerating the throughput in crop production. This thesis contributes to the automation of plant growth analysis. First, we present a novel system for automated and non-invasive/non-contact plant growth measurement. We exploit the recent advancements of sophisticated robotic technologies and near infrared laser scanners to build a 3D imaging system and use state-of-the-art Computer Vision algorithms to fully automate growth measurement. We have set up a gantry robot system having 7 degrees of freedom hanging from the roof of a growth chamber. The payload is a range scanner, which can measure dense depth maps (raw 3D coordinate points in mm) on the surface of an object (the plant). The scanner can be moved around the plant to scan from different viewpoints by programming the robot with a specific trajectory. The sequence of overlapping images can be aligned to obtain a full 3D structure of the plant in raw point cloud format, which can be triangulated to obtain a smooth surface (triangular mesh), enclosing the original plant. We show the capability of the system to capture the well known diurnal pattern of plant growth computed from the surface area and volume of the plant meshes for a number of plant species. Second, we propose a technique to detect branch junctions in plant point cloud data. We demonstrate that using these junctions as feature points, the correspondence estimation can be formulated as a subgraph matching problem, and better matching results than state-of-the-art can be achieved. Also, this idea removes the requirement of a priori knowledge about rotational angles between adjacent scanning viewpoints imposed by the original registration algorithm for complex plant data. Before, this angle information had to be approximately known. Third, we present an algorithm to classify partially occluded leaves by their contours. In general, partial contour matching is a NP-hard problem. We propose a suboptimal matching solution and show that our method outperforms state-of-the-art on 3 public leaf datasets. We anticipate using this algorithm to track growing segmented leaves in our plant range data, even when a leaf becomes partially occluded by other plant matter over time. Finally, we perform some experiments to demonstrate the capability and limitations of the system and highlight the future research directions for Computer Vision based plant phenotyping