A model-based approach for compound leaves understanding and identification

Cerutti, Guillaume; Tougne, Laure; Mille, Julien; Vacavant, Antoine; Coquin, Didier

doi:10.1109/icip.2013.6738302

Cited by 24 publications

(21 citation statements)

References 14 publications

(14 reference statements)

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“…In the context of leaf image classification and segmentation, researchers have investigated using hand-crafted features and structural operators (Wang et al, 2008;Kumar et al, 2012;Cerutti et al, 2013;Elhariri et al, 2014). Wang et al (2008) segment leaf images by using morphological operators and shape features and apply a moving center hypersphere classifier to infer the plant species.…”

Section: Related Workmentioning

confidence: 99%

“…They extract curvature features and compare them with a given database to find the best match with a labeled type. To cover a variety of leaf shapes, Cerutti et al (2013) apply a deformable leaf model and use morphological descriptors in order to classify their species. Elhariri et al (2014) compare a Random Forest classifier and a linear discriminant analysis based approach in their work for classifying 15 plant species by analyzing leaf images.…”

Section: Related Workmentioning

confidence: 99%

“…We target sugar beets as crops, which is an important crop in Northern Europe. To build up our classification system we provide an end-to-end approach where no hand-crafted features are used, as done in most of the previous work on this topic (Lottes et al, 2016a;Wang et al, 2008;Kumar et al, 2012;Cerutti et al, 2013;Elhariri et al, 2014;Perez-Ortiz et al, 2016). The only preprocessing step we perform separates vegetation from background such as soil, etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Blob-Wise Sugar Beets vs Weeds Classification for Monitoring Fields Using Convolutional Neural Networks

Milioto

Lottes

Stachniss

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

116

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ABSTRACT:UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Blob-Wise Sugar Beets vs Weeds Classification for Monitoring Fields Using Convolutional Neural Networks

Milioto

Lottes

Stachniss

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

116

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“…Notice that there are a few recent studies [34,35]- [6,7] that are closely related to our work, specifically regarding the detection of the base and apex and the division of the dataset into simple and compound leaves before identifying the ultimate species. Sfar et al [34,35] propose a coarse-to-fine strategy by learning consecutively the base and apex positions, the leaf type (simple or compound), the genera and finally the species.…”

Section: Comparison To Two Closely-related Methodsmentioning

confidence: 96%

“…A single classifier is defined for the key points detectors while for each categorisation stage, there are as classifiers as categories and each one is trained to discriminate this category from the rest (in the same stage). Cerutti et al [6,7] propose an active polygonal model in order to estimate the shape of simple leaves [7] and leaflets [6] (by estimating the length, width, bilateral width, the base and apex angles of each leaf (or leaflets). For each stage, an energy function, based on a color dissimilarity map, is minimized.…”

Section: Comparison To Two Closely-related Methodsmentioning

confidence: 99%

Semantic-based automatic structuring of leaf images for advanced plant species identification

Mzoughi

Yahiaoui

Boujemaa

et al. 2015

Multimed Tools Appl

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Structuring the search space based on domain-specific vocabulary (or concepts) is capital for enhanced image retrieval. In this paper, we study the opportunities and the impact of exploiting such a strategy in a particular problem which is the leaf species identification. We believe that such a solution is promising to reduce the effect of the high variability across and within species and define more specific and relevant leaf image representations. Among botanical concepts that describe leaves (and particularly their architecture), we focus mainly on three of the most basic and commonly-used concepts: the leaf arrangement, lobation and partition. These concepts define two different structuring types:(1) One is a coarse categorisation of leaf datasets into three subsets, namely simple lobed, simple not lobed and compound (2) The other is a decomposition of the entire leaf images into semantic regions (or parts). We perform the whole structuring process automatically by defining simple geometric parameters (extracted from the leaf contour) based on the analysis of botanical definitions. A fine recognition process is then established in order to determine the species identity. It is defined, typically, using standard (texture or contour) descriptors followed by a KNN classifier. Enriched by the proposed structuring process, the search for species candidates will be restricted to the correspondent category of the query and based on a fusion of each part responses. Experiments carried out on the Scan Pictures of the ImageCLEF 2011 dataset (3070 images totalling 70 species) have shown an increase in performances for different descriptor configurations compared to global leaf representations as well as to some recent related studies.

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Effective Vision‐based Classification for Separating Sugar Beets and Weeds for Precision Farming

Lottes

Hörferlin

Sander

et al. 2016

Journal of Field Robotics

105

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The use of robots in precision farming has the potential to reduce the reliance on herbicides and pesticides through selectively spraying individual plants or through manual weed removal. A prerequisite for that is the ability of the robot to separate and identify the value crops and the weeds in the field. Based on the output of the robot's perception system, it can trigger the actuators for spraying or removal. In this paper, we address the problem of detecting sugar beet plants as well as weeds using a camera installed on a mobile field robot. We propose a system that performs vegetation detection, local as well as object-based feature extraction, random forest classification, and smoothing through a Markov random field to obtain an accurate estimate of crops and weeds. We implemented and thoroughly evaluated our system using a real farm robot in different sugar beet fields, and we illustrate that our approach allows for accurately identifying weeds in a field. C 2016 Wiley Periodicals, Inc.

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A model-based approach for compound leaves understanding and identification

Cited by 24 publications

References 14 publications

Real-Time Blob-Wise Sugar Beets vs Weeds Classification for Monitoring Fields Using Convolutional Neural Networks

Real-Time Blob-Wise Sugar Beets vs Weeds Classification for Monitoring Fields Using Convolutional Neural Networks

Semantic-based automatic structuring of leaf images for advanced plant species identification

Effective Vision‐based Classification for Separating Sugar Beets and Weeds for Precision Farming

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