Fungus image identification using K-Nearest Neighbor

Rahmat, R F; Aruan, Toni Rizki; Purnamawati, Sarah; Faza, Sharfina; Lini, Tifani Zata; Onrizal, Onrizal

doi:10.1088/1757-899x/420/1/012097

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…3.3. K-Nearest Neigbor K-nearest neighbor algorithm is one of the classification algorithms in recognizing patterns [32]. This algorithm classifies based on the class that has the lowest distance value or the highest similarity value than other classes.…”

Section: Haralick Features Extractionmentioning

confidence: 99%

Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

Widodo¹,

Hernando

Mahmudy

2019

Sig.Img.Proc.Lett

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Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.

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Section: Haralick Features Extractionmentioning

confidence: 99%

Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

Widodo¹,

Hernando

Mahmudy

2019

Sig.Img.Proc.Lett

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“…The features of this cutting-edge machine learning algorithm are simplicity, computational efficiency, and good learning performance [22]. Fast Learning Network performance was evaluated in comparison to Support Vector Machine (SVM) [23], Decision Tree (DT) [24], Extreme Learning Machine (ELM) [25], Random Forest regressor (RFR) [26], as well as K Nearest Neighbor (KNN) [27]. Following is the organization of the remaining text.…”

Section: Introductionmentioning

confidence: 99%

Performance of fast learning approach to predicting black fungus diseases

Salman

2022

IJCEAE

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Microscopic examination can be used to make a preliminary diagnosis of fungal infections. Due to their apparent similarities, it frequently does not allow for the species to be identified clearly. Therefore, additional biochemical tests are typically required. This adds extra expenses and can make the identification process last up to ten days. Given the high death rate for immunosuppressed patients, such a delay in the adoption of targeted therapy could have serious consequences. The fast learning network method is an alternative that provides information with a unique approach to predicting black fungus. The experimental results of prediction showed that the performance of the fast learning method is superior as compared with the five algorithms used in this paper.

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Evrişimli Sinir Ağları ile Mantar Görüntülerinden Mantar Türlerinin Transfer Öğrenme Yöntemiyle Tanımlanması

Elmas

2021

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Özet: Evrişimli sinir ağlarıyla mantarların tanımlanması; hayatî açıdan önemli mantarların koruma altına alınmasında ve mantar kaynaklarının sürdürülebilir yönetim planlarının yapılmasında oldukça önemlidir Ayrıca, zehirli mantarların tespit edilmesinde, mantar yetiştiricileri ile toplayıcıların mantarları tanımlamasında yararlı olabilecek bir problem çözümüdür. Mantar şapkaları döküntü, leke, pul, kuşak, yiv, özgün desen ve renk gibi çok sayıda ayırt edici özelliği sahiptirler. Bu nedenle şapka görüntülerinin Lamel, Yüzük, Sap ve Volva görüntülerine göre tanımlamaya daha çok katkısı olacağı düşünülmüştür. Bunun yanı sıra mevcut veri setleri evrişimli sinir ağları'nı eğitmek için yetersiz veya düzensizdir. Bu tezleri ispat etmek ve çalışmalara katkı sağlamak amacıyla, 18 aile ait 472 sınıfın görüntülerini içeren üç adet yeni veri seti oluşturulmuştur. Düzensiz veri seti 148318, düzenlenmiş Şapka, Lamel, Yüzük, Sap ve Volva görüntülerini içeren veri seti 97450 görüntü içermektedir. Düzenlenerek oluşturulmuş mantar şapka görüntüleri ise 65419 görüntüden oluşmaktadır. Çalışmada 6 evrişimli sinir ağı, transfer öğrenme yöntemi kullanılarak eğitilmiştir. Düzenlenmiş mantar şapka veri setiyle eğitilmiş en başarılı ağın doğruluk oranı %97.62'dir. Bu veri setinin görüntü sayısı, birinci veri setine göre %44, ikinci veri setine göre %32 daha az olmasına rağmen başarı oranı birinci veri setine göre %26.53, ikinci veri setine göre %14.5 daha iyidir.

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Fungus image identification using K-Nearest Neighbor

Cited by 4 publications

References 2 publications

Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

Performance of fast learning approach to predicting black fungus diseases

Evrişimli Sinir Ağları ile Mantar Görüntülerinden Mantar Türlerinin Transfer Öğrenme Yöntemiyle Tanımlanması

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