Optik Mikroskopi ve Derin Öğrenme Kullanılarak Kepek Katmanından Buğday Tohumlarının Tanımlanması

Year 2025, Volume: 18 Issue: 3, 349 - 352

Yıldıray Anagün , Şahin Işık , Murat Olgun , Okan Sezer

https://doi.org/10.46309/biodicon.2025.1656264

Abstract

Özet

Tohum analizi ve sınıflandırması, ekimden önce kullanılacak tohumların çeşitlerinin belirlenmesi ve ekim yapılacak alana uygun çeşitlerin kullanıldığının doğrulanması açısından son derece önemlidir. Ekim yapılan tarımsal alanlarda yüksek verimlilik ile birlikte kaliteli ve saf tohumların elde edilebilmesi ancak ekim öncesi uygulanacak analiz ve sınıflandırma ile mümkün olabilmektedir. Bu çalışmada farklı kalitelerdeki dokuz buğday çeşidine ait tohumları sınıflandırılarak ekimden önce tohumların çeşitlerini belirleyecek bir sistem oluşturulması amaçlanmıştır. Çalışmanın temel hedefleri, bilgisayar destekli akıllı sistemler kullanarak tahıl ürünlerini otomatik olarak tanımlamak ve ekilecek bölgenin ekolojik koşullarına en uygun buğday tohumlarını belirlemektir. Bu amaçla, incelenen buğday çeşitlerinin tohum kesitleri ışık mikroskobu ile fotoğraflanarak özel bir veri seti oluşturulmuş ve yüzeysel ile derin mimariler kullanılarak CNN tabanlı bir otomatik buğday tanımlama çerçevesi önerilmiştir. En iyi sonuçlar %97,67 test doğruluğu, %90,03 duyarlılık, %98,79 özgüllük, %90,50 hassasiyet ve %90,06 f1-skoru olarak elde edilmiştir. Deneyler, CNN tabanlı yöntemlerin buğday kepeğinin ayırt edici özelliklerini çıkarmada ve buğday tohumlarını tanımlamada başarılı olduğunu göstermektedir.

Anahtar kelimeler: buğday;sınıflandırma;optik mikroskopi;derin öğrenme;tohum analizi

Keywords

Optik Mikroskopi , Derin Öğrenme , Tohum Analizi , Buğday , Sınıflandırma

Identification of wheat seeds from bran layer using optical microscopy and deep learning

Abstract

Purpose: This study aims to automate the identification of grain varieties and select the most suitable wheat genotypes for specific ecological conditions using Artificial Intelligence (AI)-based systems. The goal is to facilitate high-yield and high-quality production through pre-sowing analysis.
Method: Seeds from nine wheat genotypes with different qualities were used, and cross-sections of the wheat genotypes were photographed under a light microscope to create a specialized dataset. A Convolutional Neural Network (CNN)-based automated wheat identification framework was then proposed, utilizing both shallow and deep architectures.
Findings: The experiments confirm that CNN-based methods are highly effective in extracting distinctive features from wheat bran and accurately identifying wheat seed varieties.
Conclusion: The research successfully distinguished nine varieties and found that a simpler model (ResNet18) outperformed deeper networks, offering a practical solution for agricultural verification.

Keywords: wheat;classification;optical microscopy;deep learning;seed analysis

Keywords

wheat , classification , optical microscopy , deep learning , seed analysis

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