Inria's participation at imageclef 2013 plant identification task

2014

Our aim is to implement a plant identification application that can run on smartphones, and this shared task includes it. After the plant identification task of 2013 we concluded that the most frequent trees (e. g. in Hungary) can be identified well by a leaf, when there is a white paper background behind it at the time of photographing. This is why we want to classify more accurately this kind of pictures. Our other important goal is to develop a system which can be trained online while smartphone users take more and more photos. In the previous year there was a separate shared task for the 'Sheet as Background' photos, how- ever this year the task is very complex, and supposes solutions for hard image processing problems, not just feature extraction and classification solutions.

2014

This paper describes the participation of Inria within the Pl@ntNet project 7 at the LifeCLEF2014 plant identification task. The aim of the task was to produce a list of relevant species for each plant observation in a test dataset according to a training dataset. Each plant observation contains several annotated pictures with organ/view tags: Flower, Leaf, Fruit, Stem, Branch, Entire, Scan (exclusively of leaf). Our system treated independently each category of organ/view and then a late hierarchical fusion is used in order to combine the results on visual content analysis from the most local level analysis in pictures to the highest level related to a plant observation. For the photographs of flowers, leaves, fruits, stems, branches and entire views of plants, a large scale matching approach of local features extracted using different spatial constraints is used. For scans, the method combines the large scale matching approach with shape descriptors and geometric parameters on shape boundary. Then, several fusion methods are experimented through the four submitted runs in order to combine hierarchically the local responses to the final response at the plant observation level. The four submitted runs obtained good results and got the 4th to the 7th place over 27 submitted runs by 10 participating teams.

2012

We describe our participation in the plant identification task of ImageClef 2012. We submitted two runs, one fully automatic and another one where human assistance was provided for the images in the photo category. We have not used the meta-data in either one of the systems, for exploring the extent of image analysis for the plant identification problem. Our approach in both runs employs a variety of shape, texture and color descriptors (117 in total). We have found shape to be very discriminative for isolated leaves (scan and pseudoscan categories), followed by texture where mathematical morphology has been our main methodology. While we have used color, we did not found color information to be very useful. Mathematical morphology is also used in the watershed algorithm used for segmentation, in slightly different forms for automatic and human assisted systems. Our systems have obtained the best overall results in both the automatic and manual categories, with 43% and 45% identification accuracies respectively. We have also obtained the best results on the scanned image category, with 58% accuracy, while the 2nd best system had a 49% accuracy.

2011

We describe our participation in the plant identification task of ImageClef 2011. Our approach employs a variety of texture, shape as well as color descriptors. Due to the morphometric properties of plants, mathematical morphology has been advocated as the main methodology for texture characterization, supported by a multitude of contour-based shape and color features. We submitted a single run, where the focus has been almost exclusively on scan and scan-like images, due primarily to lack of time. Moreover, special care has been taken to obtain a fully automatic system, operating only on image data. While our photo results are low, we consider our submission successful, since besides being our first attempt, we rank 1 st considering the average of the scan and scanlike results, upon which we had concentrated our efforts.

HAL (Le Centre pour la Communication Scientifique Directe), 2011

ImageCLEFs plant identification task provides a testbed for the system-oriented evaluation of tree species identification based on leaf images. The aim is to investigate image retrieval approaches in the context of crowdsourced images of leaves collected in a collaborative manner. This paper presents an overview of the resources and assessments of the plant identification task at ImageCLEF 2011, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results.

This paper describes the participation of DAEDALUS at ImageCLEF 2011 Plant Identification task. The task is evaluated as a supervised classification problem over 71 tree species from the French Mediterranean area used as class labels, based on visual content from scan, scan-like and natural photo images. Our approach to this task is to build a classifier based on the detection of keypoints from the images extracted using Lowe's Scale Invariant Feature Transform (SIFT) algorithm. Although our overall classification score is very low as compared to other participant groups, the main conclusion that can be drawn is that SIFT keypoints seem to work significantly better for photos than for the other image types, so our approach may be a feasible strategy for the classification of this kind of visual content.

ImageCLEF's plant identification task provides a testbed for the system-oriented evaluation of tree species identification based on leaf images. The aim is to investigate image retrieval approaches in the context of crowdsourced images of leaves collected in a collaborative manner. This paper presents an overview of the resources and assessments of the plant identification task at ImageCLEF 2011, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results.

IEEE Access, 2019

Plant species recognition using leaf images is a highly important and challenging issue in botany and pattern recognition. A center problem of this task is how to accurately extract leaf image characteristics and quickly calculate the similarity between them. This article presents a new shape description approach called triangle-distance representation (TDR) for plant leaf recognition. The TDR descriptor is represented by two matrices: a sign matrix and a triangle center distance matrix. The sign matrix is used to characterize the convex/concave property of a shape contour, while the triangle center distance matrix is used to represent the bending degree and spatial information of a shape contour. This method can effectively capture the detailed and global characteristics of a leaf shape while keeping the similarity transformations (translation, rotation, and scaling) unchanged. In addition, this method is quite compact and has low computational complexity. We tested our method on four standard plant leaf datasets, including the famous Swedish, Smithsonian, Flavia, and ImageCLEF2012 datasets. The results confirm that our approach exceeds the prior state-of-the-art shape-based plant leaf recognition approaches. An extra experiment on the MPEG-7 shape dataset further shows that our method can be applied to general shape recognition.

2011

The leaves are one of the most important main sources used for plant identication. Because of this the ImageCLEF 2011 proposed a challenge based on leaf analysis for plant identication. This paper reports the experiment results of the IFSC/USP team in participating of this task. The main goal is investigate the performance of Complex Network method for feature extraction and classication of plant species. The achieved results are promising and can help the botanists in the future.

IRJET, 2022

In both botanical taxonomy and computer vision, plant image recognition has become a transdisciplinary priority. We use convolutional deep neural networks to identify the types of plants photographed in the image and to explore various factors that affect the performance of these networks. Determining plant species from field observations requires a large botanical specialist, which puts more than the reach of many nature lovers. Discovery of traditional plants is virtually impossible for the general public and is a challenge even for professionals who deal with daily crop problems such as conservationists, farmers, foresters, and architects. Even for botanists themselves, the species identification is often difficult task. This change enhances the accuracy of the model significantly. This model is tested in a leaf set of Flavia leaf sets. The results of the identification phase show that the proposed CNN model is still effective in leaf recognition with an optimal accuracy of more than 88.22%.