Tarımda çok sınıflı yabancı ot türlerinin tanımlanması için derin öğrenmenin kullanımı

Yıl 2025, Cilt: 14 Sayı: 1, 1 - 1

Ebru Ergün

https://doi.org/10.28948/ngumuh.1495040

Öz

Yabancı ot türlerinin etkili bir şekilde tespiti, verimli tarımsal yönetim için kritik öneme sahiptir ve hedefe yönelik yok etme ve optimize edilmiş tarım uygulamalarını mümkün kılar. Bu çalışmada, ResNet, VggNet ve DenseNet derin öğrenme modellerinin farklı yabancı ot türlerini doğru bir şekilde sınıflandırmadaki performanslarını değerlendirmek için kullanılmıştır. Veri seti, farklı çevresel koşullarda çekilmiş çeşitli yabancı ot türlerine ait yüksek çözünürlüklü görüntülerden oluşmaktadır. Deneysel sonuçlar, bu modellerin birden fazla yabancı ot türünü yüksek doğrulukla tespit etme yeteneğini göstermiştir. Değerlendirme metrikleri, doğruluk, hassasiyet, hatırlama ve hata matrisi, modellerin türler arasındaki ayrımı yapmadaki etkinliğini doğrulamıştır. Test edilen evrişimli sinir ağı mimarileri arasında, VggNet %99.21’lik en yüksek sınıflandırma doğruluğunu sergilemiştir. Sonuçlar, derin öğrenmeye dayalı sınıflandırma sistemlerinin, tarımsal uygulamalar için ölçeklenebilir ve verimli yabancı ot türü tespiti ve yönetimi konusunda büyük bir potansiyele sahip olduğunu vurgulamıştır.

Anahtar Kelimeler

Tarımsal yönetim, Sınıflandırma, Derin öğrenme, Çoklu sınıf, Hassas tarım, Yabancı ot türlerinin tanımlanması

Harnessing deep learning for multi-class weed species identification in agriculture

Öz

Effective identification of weed species is critical for efficient agricultural management, enabling targeted eradication and optimized farming practices. In this study, ResNet, VggNet and DenseNet were used to evaluate the performance of deep learning models in accurately classifying different weed species. The dataset consisted of high-resolution images of different weed species taken under different environmental conditions. The experimental results demonstrated the ability of these models to identify multiple weed species with high accuracy. Evaluation metrics, accuracy, precision, recall and confusion matrices, validated the effectiveness of the models in discriminating between species. Of the convolutional neural network architectures tested, VggNet showed the highest classification accuracy of 99.21%. The results underscored the potential of deep learning-based classification systems in advancing scalable and efficient weed species identification and management for agricultural applications.

Anahtar Kelimeler

Agricultural management, Classification, Deep learning, Multi-class, Precision agriculture, Weed species identification

Kaynakça

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