Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks
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 P and P and K. The researchers use the characteristics of Red, Green, Blue (RGB) color and Sobe...
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| Format: | Article |
| Language: | English |
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Bina Nusantara University
2020-05-01
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| Series: | CommIT Journal |
| Subjects: | |
| Online Access: | https://journal.binus.ac.id/index.php/commit/article/view/5952 |
| _version_ | 1797765311282282496 |
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| author | Arie Qur'ania Prihastuti Harsani Triastinurmiatiningsih Triastinurmiatiningsih Lili Ayu Wulandhari Alexander Agung Santoso Gunawan |
| author_facet | Arie Qur'ania Prihastuti Harsani Triastinurmiatiningsih Triastinurmiatiningsih Lili Ayu Wulandhari Alexander Agung Santoso Gunawan |
| author_sort | Arie Qur'ania |
| collection | DOAJ |
| description | 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 P and P and K. The researchers 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. |
| first_indexed | 2024-03-12T20:09:21Z |
| format | Article |
| id | doaj.art-0ea7354bfc084903b1ff7ae8e0c2b10d |
| institution | Directory Open Access Journal |
| issn | 1979-2484 |
| language | English |
| last_indexed | 2024-03-12T20:09:21Z |
| publishDate | 2020-05-01 |
| publisher | Bina Nusantara University |
| record_format | Article |
| series | CommIT Journal |
| spelling | doaj.art-0ea7354bfc084903b1ff7ae8e0c2b10d2023-08-02T01:50:12ZengBina Nusantara UniversityCommIT Journal1979-24842020-05-01141233010.21512/commit.v14i1.59525148Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural NetworksArie Qur'ania0Prihastuti Harsani1Triastinurmiatiningsih Triastinurmiatiningsih2Lili Ayu Wulandhari3Alexander Agung Santoso Gunawan4Universitas PakuanUniversitas PakuanUniversitas PakuanBina Nusantara UniversityBina Nusantara UniversityThe 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 P and P and K. The researchers 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.https://journal.binus.ac.id/index.php/commit/article/view/5952color extractionedge detectionnutrient deficienciesartificial neural networks |
| spellingShingle | Arie Qur'ania Prihastuti Harsani Triastinurmiatiningsih Triastinurmiatiningsih Lili Ayu Wulandhari Alexander Agung Santoso Gunawan Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks CommIT Journal color extraction edge detection nutrient deficiencies artificial neural networks |
| title | Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks |
| title_full | Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks |
| title_fullStr | Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks |
| title_full_unstemmed | Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks |
| title_short | Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks |
| title_sort | color extraction and edge detection of nutrient deficiencies in cucumber leaves using artificial neural networks |
| topic | color extraction edge detection nutrient deficiencies artificial neural networks |
| url | https://journal.binus.ac.id/index.php/commit/article/view/5952 |
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