A neural network classifier based on prior evolution and iterative approximation used for leaf recognition

Gao, Liwen; Lin, Xiaohua; Zhong, Mingjun; Zeng, Junmin

doi:10.1109/icnc.2010.5582971

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…ANN classifier is also used in [2], using a combination of DMF with Fourier descriptors and a Shape-Defining Feature (SDF). Another form of DMF is proposed in [9], using a standardized matrix representation of the leaf shape. The features are represented by different measures and angles at different distances from the tip and the base points.…”

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 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: 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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“…Leaf recognition is one of the implementations of clustering methods using image processing [18]. The leaf is one of the objects used to recognize a plant [19,20]. Image color as a feature was also effective for identifying the leaves [21].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Grading Method for Gambier Leaves Based on Combination of Area, Perimeter, and Image Intensity Using Backpropagation Artificial Neural Network

et al. 2019

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Gambier leaves are widely used in cosmetics, beverages, and medicine. Tarantang village in West Sumatera, Indonesia, is famous for its gambier commodity. Farmers usually classify gambier leaves by area and color. They inherit this ability through generations. This research creates a tool to imitate the skill of the farmers to classify gambier leaves. The tool is a box covered from outside light. Two LEDs are attached inside the box to get maintain light intensity. A camera is used to capture the leaf image and a raspberry Pi processes the leaf features. A mini monitor is provided to operate the system. Six hundred and twenty-five gambier leaves were classified into five grades. Leaves categorized into grades 1, 2, and 3 are forbidden to be picked. Grade 4 leaves are allowed to be picked and those in grade 5 are the recommended ones for picking. Leaf features are area, perimeter, and intensity of leaf image. Three artificial neural networks are developed based on each feature. One thousand leaf images were used for training and 500 leaf images were used for testing. The accuracies of the features are about 93%, 96% and 97% for area, perimeter and intensity, respectively. A combination of rules are introduced into the system based on the feature accuracy. Those rules can give 100% accuracy compared to the farmer’s recommendation. A real time application to classify the leaves could provide classification with the same decision result as the classifying performed by the farmers.

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“… Chaki and Parekh (2011) presented a schematic for the automated detection of three classes in a plant species by analyzing the shapes of leaves and using several neural network classifiers. Gao et al (2010a) proposed a neural network classifier based on prior evolution and iterative approximation for leaf recognition. Huang and He (2008) applied probabilistic neural networks for the recognition of 30 types of broad-leaved trees.…”

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confidence: 99%

Plant identification through images: Using feature extraction of key points on leaf contours¹

Gwo¹,

Wei²

2013

Appl Plant Sci

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• Premise of the study: Because plant identification demands extensive knowledge and complex terminologies, even professional botanists require significant time in the field for mastery of the subject. As plant leaves are normally regarded as possessing useful characteristics for species identification, leaf recognition through images can be considered an important research issue for plant recognition.• Methods: This study proposes a feature extraction method for leaf contours, which describes the lines between the centroid and each contour point on an image. A length histogram is created to represent the distribution of distances in the leaf contour. Thereafter, a classifier is applied from a statistical model to calculate the matching score of the template and query leaf.• Results: The experimental results show that the top value achieves 92.7% and the first two values can achieve 97.3%. In the scale invariance test, those 45 correlation coefficients fall between the minimal value of 0.98611 and the maximal value of 0.99992. Like the scale invariance test, the rotation invariance test performed 45 comparison sets. The correlation coefficients range between 0.98071 and 0.99988.• Discussion: This study shows that the extracted features from leaf images are invariant to scale and rotation because those features are close to positive correlation in terms of coefficient correlation. Moreover, the experimental results indicated that the proposed method outperforms two other methods, Zernike moments and curvature scale space.

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A neural network classifier based on prior evolution and iterative approximation used for leaf recognition

Cited by 11 publications

References 8 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

Implementation of Grading Method for Gambier Leaves Based on Combination of Area, Perimeter, and Image Intensity Using Backpropagation Artificial Neural Network

Plant identification through images: Using feature extraction of key points on leaf contours¹

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A neural network classifier based on prior evolution and iterative approximation used for leaf recognition

Cited by 11 publications

References 8 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

Implementation of Grading Method for Gambier Leaves Based on Combination of Area, Perimeter, and Image Intensity Using Backpropagation Artificial Neural Network

Plant identification through images: Using feature extraction of key points on leaf contours1

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Plant identification through images: Using feature extraction of key points on leaf contours¹