Avokado meyve kalitesinin izlenmesi için destek vektör makinelerine dayalı bir sınıflandırma

Öz

Bilimsel olarak, bir yöntemin etkinliği, mevcut senaryo, parametre ve/veya veriler içinde belirli bir süreci takip eden bir değerlendirmeye dayalı olarak en iyi tahmin/hesaplama gücünü ifade eder. İyi bir tahmin için probleme en uygun yaklaşım(lar)ın göz önünde bulundurulması ve ilgili testlerin güvenilir bir şekilde yapılması gerekmektedir. Gıda güvenliği ve meyve kalitesi alanında yapılan uygulamalı çalışmalar, kullanılan yöntemlerin doğruluğu, hızı ve ekonomik parametrelerinin özellikle önemli olması ile birlikte kritik öneme sahiptir. Bu çalışmada, literatürde ilk kez, Arduino tabanlı bir sıcaklık ve gaz izleme sistemi (e-burun olarak isimlendirilir) ile kontrollü bir deney ortamında avokado meyvesinin çürümesi izlenerek verileri alınmakta ve çürümeyi tespit etmek (sınıflandırmak) için bir makine öğrenmesi yöntemi olan destek vektör makineleri kullanılmaktadır. Bu çalışmada, sınıflandırma için çok az eğitim verisi ile %99'un üzerinde test ve doğrulama başarısı elde edilmiştir. Elde edilen sonuçlar, geliştirilen e-burun tespit sonuçları ve soğuk hava deposunda diğer meyvelerdeki çürüme seviyesinin belirlenmesinde kullanılan yöntem açısından cesaret vericidir.

Anahtar Kelimeler

• Gıda güvenliği
• Makine öğrenmesi
• Destek vektör makineleri
• E-burun
• Meyve kalitesi
• Avokado

A classification based on support vector machines for monitoring avocado fruit quality

Abstract

Scientifically, the efficiency of a method refers to its power to best predict/calculate based on an evaluation following a certain process within the current scenario, parameter and/or data. For a good prediction, the most appropriate approach(es) to a problem should be considered and the related tests should be done reliably. Practical studies in the field of food safety and fruit quality are critical, with the accuracy, speed and economic parameters of the methods used being of particular importance. In this study, for the first time in literature an Arduino-based temperature and gas monitoring system (called e-nose) is used to monitor the decay of avocado fruit in a controlled experimental environment and support vector machines, a machine learning method, are used to detect (classification) the decay. In this study, test and validation success of over 99% was achieved with very few training-data for classification. The obtained results are encouraging in terms of the detection results of the developed e-nose and the method used to determine the level of decay in other fruit in cold storage.

Keywords

• Food safety
• Machine learning
• Support vector machines
• E-nose
• Fruit quality
• Avocado

Kaynakça

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