Identifying leaf in a natural image using morphological characters

R, James Nesaratnam; Balamurugan, C. R.

doi:10.1109/iciiecs.2015.7193115

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…On the other hand, two of the methods compared have achieved slightly higher accuracy rates than those of the proposed system. This outcome is expected as the proposed method is using simple partial shape features and MI compared to a combination of color, texture, and shape features in [13]. Similarly, by comparing the proposed method with the DMF approach in [11], the impact of using the more efficient SVM classifier is significant compared to using k-NN in the same reference.…”

Section: Methodsmentioning

confidence: 76%

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Automated Leaf Alignment and Partial Shape Feature Extraction for Plant Leaf Classification

Hamid

Al-Haddad

2019

ELCVIA

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The last few decades have witnessed various approaches to automate the process of plant classification using the characteristics of the leaf. Several approaches have been proposed, and the majority focused on global shape features. However, one challenge that faces this task is the high interclass similarity amongst the leaves of different species in terms of the global shape. Furthermore, there always has been an obstacle against full automation as several approaches require user intervention to align the leaf. Therefore, a new set of Quartile Features (QF) is proposed in this paper to describe the partial shape of the leaf, in addition to an automated alignment approach to automate the system. The QF are extracted from the horizontal and vertical leaf quartiles to describe the partial shape of the leaf and the relations among its parts. The well-known Flavia dataset has been selected for the evaluation of the proposed system. The experimental results indicate the ability of the proposed alignment algorithm to align leaves with different shapes and maintain a correct classification accuracy regardless of the orientation of the input leaf samples. Furthermore, the proposed QF indicated promising results by increasing the accuracy of the classification by a range of approximately 26% to 30% when combined with Hu's Moment Invariants, using k-fold cross-validation technique.

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Section: Methodsmentioning

confidence: 76%

“…In [12], MI are combined with Centroid Radii to classify 3 types of plants. MI are also employed in [13] as an input to an SVM classifier, and in [14] to differentiate the weed from the crop using a Nearest Neighbor Classifier (1-NN). Zernike Moment Invariants (ZMI) are another category of invariant moments.…”

Section: Related Workmentioning

confidence: 99%

Automated Leaf Alignment and Partial Shape Feature Extraction for Plant Leaf Classification

Hamid

Al-Haddad

2019

ELCVIA

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“…The SVM is obtained from the open source library of programming functions mainly aimed at real-time computer vision called OpenCV. The SVM implementation offered by this library has been widely used in other works (especially in computer vision) [ 35 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

A Semantic Labeling of the Environment Based on What People Do

Crespo

Gómez

Hernandez

et al. 2017

Sensors

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Abstract:In this work, a system is developed for semantic labeling of locations based on what people do. This system is useful for semantic navigation of mobile robots. The system differentiates environments according to what people do in them. Background sound, number of people in a room and amount of movement of those people are items to be considered when trying to tell if people are doing different actions. These data are sampled, and it is assumed that people behave differently and perform different actions. A support vector machine is trained with the obtained samples, and therefore, it allows one to identify the room. Finally, the results are discussed and support the hypothesis that the proposed system can help to semantically label a room.

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“…The classifier of SVM also proved to be effective to detect plants in their natural habitat, which was purposely designed in an environment with heavy interferences and overlapping issues [54]. The system used three plant species with 300 leaf images for the experiments with 86.7% accuracy in identification.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Review on Techniques for Plant Leaf Classification and Recognition

et al. 2019

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Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extraction appeared to improve the performance of plant leaf classification. The findings of previous studies are critically compared in terms of their accuracy based on the applied neural network techniques. This paper aims to review and analyze the implementation and performance of various methodologies on plant classification. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation.

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Identifying leaf in a natural image using morphological characters

Cited by 14 publications

References 9 publications

Automated Leaf Alignment and Partial Shape Feature Extraction for Plant Leaf Classification

Automated Leaf Alignment and Partial Shape Feature Extraction for Plant Leaf Classification

A Semantic Labeling of the Environment Based on What People Do

Review on Techniques for Plant Leaf Classification and Recognition

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