Pestisit Tespiti için Sensör Optimizasyonlu Bir Elektronik Burun: Kirazlarda Diazinon'un YSA ve Genetik Algoritma Tabanlı Sınıflandırılması

Yıl 2025, Cilt: 15 Sayı: 3, 1252 - 1263, 15.09.2025

Cemaleddin Şimşek , Ahmet Yılmaz

https://doi.org/10.31466/kfbd.1675914

Öz

Tarım, insan yaşamını sürdürmek için vazgeçilmez olan besin kaynaklarını sağlamak açısından hayati öneme sahiptir. Artan nüfusun taleplerini karşılamak ve yüksek verimli tarım elde etmek amacıyla, zirai pestisit kullanımı yaygın bir uygulama haline gelmiştir. Çeşitli pestisitler tarımsal verimliliği artırabilse de, çalışmalar bunların insan sağlığı üzerinde çeşitli etkilere sahip olabileceğini göstermiştir. Bu nedenle, bu maddelerin tespiti ve insan sağlığı üzerindeki etkilerinin en aza indirilmesi kritik öneme sahiptir. Bu çalışma, kirazlardaki Diazinon pestisit kalıntılarını tespit etmek için bir elektronik burun, yapay sinir ağları (YSA) ve genetik algoritmalar (GA) kullanarak etkili bir sınıflandırma yöntemi sunmaktadır. Piyasada kolayca bulunabilen 11 gaz sensörü kullanılarak bir elektronik burun sistemi geliştirilmiştir. Farklı seviyelerde pestisit kontaminasyonu olan ve olmayan kiraz örneklerinden elde edilen veriler, bir YSA sınıflandırıcısı ile sensör tepkilerini analiz etmek için kullanılmıştır. Ardından, gereken sensör sayısını en aza indirmek için GA kullanılmıştır. Sonuçlar, yalnızca 3 sensör kullanılarak bile %100'e varan sınıflandırma doğruluğunun elde edilebileceğini göstermektedir. Bu çalışma, zirai pestisit kalıntılarının tespiti için maliyet etkin ve yüksek doğruluklu bir yöntem sunarak mevcut literatüre önemli bir katkı sağlamaktadır.

Anahtar Kelimeler

Elektronik burun , Pestisit tespiti , Yapay sinir ağı , Genetik algoritma , Sınıflandırma

A Sensor-Optimized Electronic Nose for Pesticide Detection: ANN and Genetic Algorithm-Based Classification of Diazinon in Cherries

Öz

Agriculture is vital for providing the indispensable food sources that sustain human life. To meet the demands of a growing population and achieve high-yield farming, the use of agricultural pesticides has become a widespread practice. While various pesticides can increase agricultural productivity, studies have shown they can also have diverse effects on human health. Therefore, the detection of these substances and the minimization of their impact on human health are crucial. This study introduces an effective classification method for detecting Diazinon pesticide residues in cherries using an electronic nose, artificial neural networks (ANN), and genetic algorithms (GA). An electronic nose system was developed using 11 commercially available gas sensors. Data obtained from cherry samples with varying levels of pesticide contamination, as well as pesticide-free samples, were used to analyze sensor responses with an ANN classifier. GA was then employed to minimize the number of sensors required. The results demonstrate that classification accuracy reaching 100% can be achieved using as few as 3 sensors. This study offers a cost-effective and highly accurate method for detecting agricultural pesticide residues, making a significant contribution to the existing literature.

Anahtar Kelimeler

Electronic nose , Pesticide detection , Artificial neural network , Genetic algorithm , Classification

Kaynakça

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