Classification of plant leaf images with complicated background

Wang, Xiaofeng; Huang, De-Shuang; Du, Ji-Xiang; Xu, Huan; Heutte, Laurent

doi:10.1016/j.amc.2008.05.108

Cited by 201 publications

(103 citation statements)

References 16 publications

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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%

“…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. Kumar et al (2012) start from segmented images of leaves.…”

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%

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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%

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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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“…Due to leaf easiness to access, carry and process; plant species recognition based on leaves has been by far the most popular methods reported in the literature [6,7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Variant-Order Statistics based Model for RealTime Plant Species Recognition

Eid¹,

Darwish²

2017

IJITCS

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Abstract-There are an urgent need of categorizing plant by its species, to help botanist setting up a plant species database. However, plant recognition model is still very challenging task in computer vision and can be onerous and time consuming because of inefficient representation approaches. This paper, proposes a recognition model for classifying botanical species from leaf images, using combination of variant-order statistics based measures. Hence, the spatial coordinates values of gray pixels defines the qualities of texture, for the proposed model a gray-scale approach is adopted for analyzing the local patterns of leaves images using second and higher order statistical measures. While, first order statistical measures are used to extract color descriptors from leaves images.Evaluation of the proposed model shows the importance of combining variant-order statistics measures for enhancing the plant leaf recognition accuracy. Several experiments on Flavia dataset and swedish dataset are conducted. Experimental results indicates that; the proposed model yields to improve the recognition rate up to 97.1% and 94.7% for both Flavia and Swedish dataset respectively; while taking less execution time.

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“…Since the 1980s, numerous studies on leaf segmentation have been conducted (Guyer et al, 1984), including recent development of genetic and watershed algorithms for leaf information extraction (Lee & Slaughter, 2004;Neto, Meyer, & Jones, 2006;Tang, Zhao, & Tao, 2009;Wang, Ding, & Fang, 2008;Wang, Huang, Du, Xu, & Heutte, 2008). In addition, spectral images and stereo vision techniques have been introduced to solve problems of leaf extraction and segmentation of overlapping leaves (Noble & Brown, 2008;Teng, Kuo, & Chen, 2011).…”

Section: Introductionmentioning

confidence: 99%

Plant leaf detection using modified active shape models

Xia

Lee

et al. 2013

Biosystems Engineering

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We propose an in situ detection method of multiple leaves with overlapping and occlusion in greenhouse conditions. Initially a multilayer perceptron (MLP) is used to classify partial boundary images of pepper leaves. After the partial leaf boundary detection, active shape models (ASMs) are subsequently built to employ the images of entire leaves based on a priori knowledge using landmark. Two deformable models were developed with pepper leaves:Boundary-ASM and MLP-ASM. Matching processes are carried out by deforming the trained leaf models to fit real leaf images collected in the greenhouse. MLP-ASM detected 76.7 and 87.8% of overlapping and occluded pepper leaves respectively, while Boundary-ASM showed detection rates of 63.4 and 76.7%. The detection rates by the conventional ASM were 23.3 and 29.3%. The leaf models trained with pepper leaves were further tested with leaves of paprika, in the same family but with more complex shapes (e.g., holes and rolling). Although the overall detection rates were somewhat lower than those for pepper, the rates for the occluded and overlapping leaves of paprika were still higher with MLP-ASM (ranging from 60.4 to 76.7%) and Boundary-ASM (ranging from 50.5 to 63.3%) than using the conventional active shape model (from 21.6 to 30.0%). The modified active shape models with the boundary classifier could be an efficient means for detecting multiple leaves in field conditions. ª 2013 IAgrE. Published by Elsevier Ltd. All rights reserved. IntroductionIn conventional farming, agricultural cultivation and pest management are conducted manually by farmers, causing human health problems and productivity loss. Recently, precision agriculture has been proposed to reduce the chemical stress to humans and agricultural products, relying on new technologies (Zhang, Wang, & Wang, 2002), such as computer vision and robotics. Proper judgement of leaf status is essential for effective management of crops including cultivation and crop protection. Automatic detection of individual leaves is a fundamental * Corresponding author. Tel.: þ82 51 5102261; fax: þ82 51 5812962.E-mail address: tschon@pusan.ac.kr (T.-S. Chon).Available online at www.sciencedirect.com journal homepa ge: www .e lsev ie r.com/locate/issn/153 75110 b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 3 e3 5

show abstract

Classification of plant leaf images with complicated background

Cited by 201 publications

References 16 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

Variant-Order Statistics based Model for RealTime Plant Species Recognition

Plant leaf detection using modified active shape models

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