Pattern recognition on herbs leaves using region-based invariants feature extraction

Isnanto, R. Rizal; Zahra, Ajub Ajulian; Julietta, Patricia

doi:10.1109/icitacee.2016.7892491

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…The result shows that the combination of these features using the backpropagation neural network had a significant performance in grading gambier leaves. Plantscan dataset [28] Moment Invariant Feed Forward Neural Network 95.5% Plantscan dataset [28] Centroid Radii 100% Plantscan dataset [28] Moment Invariant and Centroid Radi 100% Leaf [29] Leaf Contour Convolutional Neural Network 94% Herbal Leaf [20] Shape Euclidian 86.67% Canberra Distance 72% Gonzales dataset and Wood website [30] Leaf area, circumference, centroid, major axis length, minor axis length…”

Section: Testing Performancementioning

confidence: 99%

“…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%

“…These features could be implemented to the support vector machine method with a good performance. Laos leaves were specially studied by Reference [20]. The study used moment invariant as the feature to recognize Laos leaves with different positions.…”

Section: Introductionmentioning

confidence: 99%

“…A moment invariant feature using a neural network method to classify leaves had about 93% accuracy. Centroid representation as an addition increased this accuracy to 100% [20]. In Reference [23], gross shape, Fourier, and Multiscale Distance Matrix-Average Distance (MDM-A) were the features used to recognize different types of leaves from Flavia and Swedish Leaf Image Database (SLID) datasets.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Testing Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Besides, drugs produced from herbal plants are also safer to consume than drugs made from chemicals. However, because of the many types of herbal plants and limited information (Hidanti et al, 2016) and the knowledge of herbal plants that cause disease treatment choices always fall on drugs that contain chemicals (Hidanti et al, 2016) whereas chemicals are inorganic and pure, whereas the human body is organic and complex so chemical drugs are often not suitable and effective (Ni'mah et al, 2018a) to cure certain diseases even some chemical drugs are only symptomatic or temporary and must be taken for life by the patient (Ni'mah et al, 2018b). To provide information to the public as well as medical personnel about herbal plants, we need a system that can identify and recognize herbal plants based on one part of the plant.…”

Section: Introductionmentioning

confidence: 99%

Identification of Herbal Plant Based on Leaf Image Using GLCM Feature and K-Means

Anggraini

Wati

Shidiq

et al. 2020

techno

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Medicinal plants are one of the groups of plants that have enormous benefits for humans because they can help the medical process for healing disease. Herbal plants can be used as ingredients for medicines, medicines produced from herbal plants are also natural. Lack of knowledge of herbal plants causes people to prefer chemical-based medicines to help cure their diseases, even though chemical-based drugs have side effects on human health. This study aims to identify types of herbal plants based on the extraction of contrast, correlation, energy, and homogeneity features as well as shape recognition based on metric and eccentricity values. The method used in this research is GLCM features and K-means clustering. In this study, the data used consisted of 352 data divided into 320 training data and 32 testing data. This research succeeded in identifying and classifying herbal plant species using GLCM features and K-means clustering segmentation with an average accuracy value of 85.94%.

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Preethi

Cherukuri

2018

Advances in Intelligent Systems and Computing

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Pattern recognition on herbs leaves using region-based invariants feature extraction

Cited by 8 publications

References 6 publications

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

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

Identification of Herbal Plant Based on Leaf Image Using GLCM Feature and K-Means

Privacy Preserving Hu’s Moments in Encrypted Domain

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