An android application for leaf-based plant identification

Mouine, Sofiène; Yahiaoui, Itheri; Verroust-Blondet, Anne; Joyeux, Laurent; Selmi, Souheil; Goëau, Hervé

doi:10.1145/2461466.2461520

Cited by 16 publications

(4 citation statements)

References 6 publications

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“…For future works, we will integrate more advanced image features with our framework and implement our system as a mobile application to achieve mobile visual search (e.g., [7]- [8]). The proposed framework via sparse coding can be also extended to the applications of recognizing other types of images/videos.…”

Section: Resultsmentioning

confidence: 99%

“…Most recently developed plant identification frameworks rely on content-based image retrieval techniques (usually with k-nearest neighbor, i.e., k-NN, classification) [1]- [8], while several ones are based on neural network-based classification [9]- [10]. Moreover, a novel classification method, called move median centers (MMC) hypersphere classifier was proposed in [11], where another similar one called moving center hypersphere (MCH) classifier was proposed in [11].…”

Section: Introductionmentioning

confidence: 99%

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Comparative study of leaf image recognition with a novel learning-based approach

Hsiao

Chang

et al. 2014

2014 Science and Information Conference

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Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse codingbased framework can perform better.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative study of leaf image recognition with a novel learning-based approach

Hsiao

Chang

et al. 2014

2014 Science and Information Conference

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“…Products and research related to mobile applications for plant identification exist today. They focus on visual recognition technology: Belhumeur et al [2], Leafsnap [5], Works of INRIA [12,8], Angelova et al [1], and Hanashirabe [4]. These technologies will be helpful extensions for the KOKOPIN platform.…”

Section: Discussionmentioning

confidence: 99%

KOKOPIN app

Mishima

Matsumoto

Takano

et al. 2013

Proceedings of the 2nd ACM International Workshop on Multimedia Analysis for Ecological Data

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KOKOPIN is a social platform enabling field research of biodiversity using an iPhone application. Our application's camera and meta-data tagging functions facilitate easy data collection. Customizable groups are another unique feature of this system. This paper introduces our initial design process, and some on-going field projects using KOKOPIN. KOKOPIN's development was motivated by the Dandelion Survey in West Japan, a participatory biodiversity research project. The system consists of an iPhone App and backend servers. The platform was designed to accept a variety of data types optimized by a project administrator. As examples of usage, biodiversity data from the Campus Flora Project and the Wooden Assets Management of the Kyushu University Museum were reported.The application was designed as a versatile platform, and recently it started to be accepted not only for biogeography research but also for non-biological services.

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“…The process can, however, be challenging even to these skilled persons due to the vast number of species which share similar physical traits, such as stems, roots, flowers, and leaves (Hsiao et al, 2014). Further, the similarity between species and the variety within species renders the identification process particularly difficult and time-consuming (Mouine et al, 2013). According to Mäder et al (2021), the accurate identification of species in the field requires adequate individual skills, making biodiversity monitoring a labour-intensive task.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Accuracy of Thirteen Photo-based Applications in the Identification of Trees Species in Northern Ghana

2023

IJFMR

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Photo-based tree identification applications have gained prominence for their convenience and speed of identifying tree species. However, the accuracy of these applications remains a critical concern. This study assessed the accuracy of thirteen photo-based tree identification applications in the identification of twenty-two tree species in the savannah ecological zone of Ghana using their leaf samples. Three photographs were taken from well-developed and diseased-free leaves from each tree, in their natural environment, using a Samsung Galaxy Tab S4 phone with inbuilt camera. The photographs were uploaded into the respective Applications’ platforms for processing and identification. This study used 858 photographs, from 22 plant species using 13 photo-based applications. The number of times an application was able to identify the species were recorded and analysed using Microsoft Excel. The results showed that mean identification accuracy was 65.03%. Seven of the applications were able to identify the tree species up to 72.73 - 95.45% accuracy. The remaining eight had an accuracy range of 9.90 -50.09%. LeafSnap, PlantNet and PlantID had the same and the highest accuracy rate of 95.45% corresponding to 21 of the plant species identified. These were closely followed by Plant ID, PictureThis, Google Lens and Nature ID with accuracy rates of 86.36%, 81.81%, 77.27% and 72.73%, respectively. Plant-X had the least accuracy of 9.09%. The study recommended applications with identification accuracy above 70% for used in identifying species in the Savanna Zone of Ghana. The results of this study have significant implications for the identification and management of tree species as non-taxonomists, non-botanists and non-naturalists will be able to work effectively. Further studies, consideration up to the family level, are needed to further improve the accuracy of these applications. The study also recommends the inclusion of other plant organs such as the fruits, flowers and bark in future research works. It is also recommended that other Photo-based tree identification applications be studied, and the results compared with findings of this study to assist select the best applications for identifying trees in the Savanna Zone of Northern Ghana.

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An android application for leaf-based plant identification

Cited by 16 publications

References 6 publications

Comparative study of leaf image recognition with a novel learning-based approach

Comparative study of leaf image recognition with a novel learning-based approach

KOKOPIN app

Comparative Analysis of the Accuracy of Thirteen Photo-based Applications in the Identification of Trees Species in Northern Ghana

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