Weed identification using an automated active shape matching (AASM) technique

Swain, Kishore Chandra; Nørremark, Michael; Jørgensen, Rasmus Nyholm; Midtiby, Henrik Skov; Green, Ole

doi:10.1016/j.biosystemseng.2011.09.011

Cited by 43 publications

(31 citation statements)

References 20 publications

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“…Since agricultural practices (i.e., continuous, steady plant inspection) may not require extremely short time performance, the proposed methods could be still applied to practical situations in reasonable time scale. The run-time could be significantly reduced by implementing the algorithm in Cþþ (Swain et al, 2011). Further development may be needed to reduce calculation cost.…”

Section: Discussionmentioning

confidence: 99%

“…Manh, Rabatel, Assemat, and Aldon (2001) developed a deformable template to accumulate information on weed leaves on the basis of the tips of leaves, while De Meezo, Rabatel, and Fiorio (2003) reported another shape-guided approach for leaf segmentation. Recently, Active Shape Models (ASMs) have been introduced for identification and classification of weed species (Søgaard, 2005;Persson & Å strand, 2008;Swain et al, 2011). An ASM is a deformable template model and has sufficient tolerance to match shape variations of the same objects using a priori knowledge, and is sufficiently robust to locate known objects in the presence of noise and occlusion (Cootes & Taylor, 1992;Cootes, Taylor, Cooper, & Graham, 1995).…”

Section: Introductionmentioning

confidence: 99%

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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

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plant leaf detection using modified active shape models

Xia

Lee

et al. 2013

Biosystems Engineering

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“…Moreover, the features have been defined consists of aspect ratio, compactness, centroid and horizontal or vertical projections. In order to classify weeds from sugar beet Jafari et al (2006) was focused on leaf colour features in his research (Swain et al, 2011). The purpose is weed colour is different from main plant and soil.…”

Section: Leaf Featuresmentioning

confidence: 99%

Leaf Features Extraction and Recognition Approaches to Classify Plant

Jabal

Hamid

Shuib

et al. 2013

Journal of Computer Science

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Plant classification based on leaf identification is becoming a popular trend. Each leaf carries substantial information that can be used to identify and classify the origin or the type of plant. In medical perspective, images have been used by doctors to diagnose diseases and this method has been proven reliable for years. Using the same method as doctors, researchers try to simulate the same principle to recognise a plant using high quality leaf images and complex mathematical formulae for computers to decide the origin and type of plants. The experiments have yielded many success stories in the lab, but some approaches have failed miserably when tested in the real world. This happens because researchers may have ignored the facts that the real world sampling may not have the luxury and complacency as what they may have in the lab. What this study intends to deliver is the ideal case approach in plant classification and recognition that not only applicable in the real world, but also acceptable in the lab. The consequence from this study is to introducing more external factors for consideration when experimenting real world sampling for leaf recognition and classification does this.

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“…Swain et al [9] developed a smart weed identification technique based on the active shape modeling concept for the morphological identification of the crop and weed. The leaf model was aligned and deformed using automated active shape matching system.…”

Section: Related Workmentioning

confidence: 99%

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

Prema¹,

Murugan²

2016

ELCVIA

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Weed management is the most significant process in the agricultural applications to improve the crop productivity rate and reduce the herbicide application cost. Existing weed detection techniques does not yield better performance due to the complex background and illumination variation. Hence, there arises a need for the development of effective weed identification technique. To overcome this drawback, this paper proposes a novel Angular Texture Pattern (ATP) Extraction Method for crop and weed discrimination using curvelet transformation. In our proposed work, Adaptive Median Filter (AMF) is used for filtering the impulse noise from the image. Plant image identification is performed using green pixel extraction and K-means clustering. Wrapping based Curvelet transform is applied to the plant image. Feature extraction is performed to extract the angular texture pattern of the plant image. Particle Swarm Optimization (PSO)-based Differential Evolution Feature Selection (DEFS) approach is applied to select the optimal features. Then, the selected features are learned and passed through an RVM-based classifier to find out the weed. Edge detection and contouring is performed to identify the weed in the plant image. The fuzzy rule-based approach is applied to detect the low, medium and high levels of the weed patchiness. From the experimental results, it is clearly observed that the accuracy of the proposed approach is higher than the existing Support Vector Machine (SVM) based approaches. The proposed approach achieves higher accuracy, sensitivity, specificity, Hausdorff distance, Jaccard distance and Dice distance.

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Weed identification using an automated active shape matching (AASM) technique

Cited by 43 publications

References 20 publications

Plant leaf detection using modified active shape models

Plant leaf detection using modified active shape models

Leaf Features Extraction and Recognition Approaches to Classify Plant

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

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