Identification of jasmine flower (<i>Jasminum sp</i>.) based on the shape of the flower using sobel edge and <i>k</i>-nearest neighbour

Qur'ania, Arie; Sarinah, I.

doi:10.1088/1757-899x/332/1/012008

Cited by 3 publications

(3 citation statements)

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“…Determination of the backpropagation network with the minimum error value for the extraction of the shapes using the detection of the Sobel edge based on table 2 was found in the 12th experiment with the layer function [15 8 1] where there were 15 inputs, 8 hidden layers, and 1 output, learning rate of 0.00001, performance function using SSE (Sum Squared Error), epoch as much as 5000 times, parameter of goal equal to 0.005, which resulted in an average error value of equal to 0.002496. The target value range is obtained by looking at the average value of the last weights of each of the training data [10]. The result of the target value can be seen in Table 3.…”

Section: Resultsmentioning

confidence: 99%

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Identification of Chrysanthemum Flower Based on Color and Flower Form using Sobel Edge and Artificial Neural Network

2019

ijrte

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Chrysanthemum is a popular ornamental plant in Indonesia because it has a variety of shapes and colors that attract a lot of attention. In addition to the trimmer, this flower can be used as a mosquito repellent and also absorbs dirty air pollution. Chrysanthemum flowers can be distinguished by color and shape with two types of ways to grow Chrysanthemum flowers that grow in clusters are called spray and also flowers that grow with no cluster or a flower per stalk. In addition, Chrysanthemum flowers have various color variations including pink, purple, yellow, white, orange, and red. The purpose of this study is to identify the type of Chrysanthemum flowers using extraction of Red, Green, Blue (RGB) color features and characteristic extraction using a Sobel edge detection, for his identification method using Artificial Neural Networks. Chrysanthemum data are white puma, Vania, Iranian, purple aster, and pink standard used in this research consisted of 100 training data and 50 tests data. The test was done with four tests that is identification with form extraction (Sobel) yielded accuracy value equal to 59.52%, identification with color and shape extraction yield accuracy value equal to 65.36%, identification with color extraction on flower and base flower plate yield value accuracy of 69.44%, and identification with color extraction yields an accuracy value of 75.35%.

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Section: Resultsmentioning

confidence: 99%

“…Research using sobel edge detection to identify jasmine flowers was done by Qur'ania and Sarinah [10]. Testing the size of a jasmine flower image that is 50 x 50 pixels, 100 x 100 pixels, 150 x 150 pixels produces an accuracy of 84%.…”

Section: Introductionmentioning

confidence: 99%

Identification of Chrysanthemum Flower Based on Color and Flower Form using Sobel Edge and Artificial Neural Network

2019

ijrte

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“…Jasmine gambir (Jasminum officinale) is a perennial plant with a creeping shrub habitus capable of growing in an open area of low and high land (Qur'ania and Sarinah 2018). In Indonesia, the production of jasmine gambier is quite potential for agribusiness industries, as its flowers are used as an important raw material in perfumery (Budianto et al 2021) to make farmers in some areas like Tegal, Purbalingga and Pemalang, of Central Java, grow this plant massively.…”

Section: Introductionmentioning

confidence: 99%

Composition and abundance level of pest mites in jasmine gambier (Jasminum officinale) plantation of Purbalingga, Central Java, Indonesia affected by some abiotic factors

Budianto

Sasongko

2022

Biodiversitas

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Abstract. Budianto BH, Sasongko ND. 2022. Composition and abundance level of pest mites in jasmine gambier (Jasminum officinale) plantation of Purbalingga, Central Java, Indonesia affected by some abiotic factors. Biodiversitas 23: 5227-5232. The decline in productivity of gambier jasmine flowers is not only caused by the reduction of gambier jasmine land area but also insect, caterpillar and pest mite attacks. Types of pest mites and their population dynamics are closely related to abiotic factor conditions such as temperature, humidity, rainfall, season, an abundance of predatory mites and types of plant cultivars. The study aimed to determine the types of pest mites and some abiotic factors that affect their abundance in gambier jasmine plants. The research method was a survey with a purposive sampling technique. Sampling locations were in Cipawon village, Bukateja sub-district, Purbalingga district, and Central Java Province, Indonesia. The obtained data were analyzed by analysis of variance at an error rate of 0.05. The types of pest mites that were identified consisted of 8 species, namely Brevipalpus phoenicis, B. californicus, B. papayensis, B. obovatus, Tetranychus urticae, T. kanzawai, T. cinnabarinus and Tyrophagus putrescentiae. The type of pest mite always found at each sampling point was B. phoenicis, with an abundance range of 0.027 to 0.067. The results of the analysis of the variance of pest mite abundance in gambier jasmine plantations showed that abundance of individuals between pest mite species did not significantly differ at all sampling points. Based on multivariate analysis, it was noted that trichome density had the most influence on pest mite abundance with a population model of Y = 0.399 + 0.043a + 0.012b.

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Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

Qur'ania

Harsani

Triastinurmiatiningsih

et al. 2020

CommIT (Communication and Information Technology) Journal

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The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and Pand P and K. The r esearchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.

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Identification of jasmine flower (Jasminum sp.) based on the shape of the flower using sobel edge and k-nearest neighbour

Cited by 3 publications

References 0 publications

Identification of Chrysanthemum Flower Based on Color and Flower Form using Sobel Edge and Artificial Neural Network

Identification of Chrysanthemum Flower Based on Color and Flower Form using Sobel Edge and Artificial Neural Network

Composition and abundance level of pest mites in jasmine gambier (Jasminum officinale) plantation of Purbalingga, Central Java, Indonesia affected by some abiotic factors

Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

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