Transfer Öğrenme Teknikleri Kullanarak Nohut Çeşidi Sınıflandırma

Year 2024, Volume: 14 Issue: 1, 48 - 58, 29.04.2024

İbrahim Kılıç Nesibe Yalçın

Abstract

Tohum saflığı, tarım üretiminde verimi artırmak ve ürün kalite standartlarını karşılamak için oldukça önemlidir. Bu durum, tohum üreticilerinden dağıtıcılarına tarım endüstrisinin, tohum saflığına daha fazla önem vermesini gerektirmektedir. Bu da tohum çeşidi sınıflandırma ve ayırma yöntemlerine ihtiyacı artırmıştır. Çalışma kapsamında, dünyada en çok üretilen yemeklik baklagillerden biri olan nohudun çeşit sınıflandırması problemi ele alınmıştır. Sınıflandırma için 14 adet ön eğitimli derin öğrenme modeli kullanılmış ve model performansları karşılaştırılarak ilgili problem için en başarılı model(ler) tespit edilmeye çalışılmıştır. Başarımı en yüksek modeller VGG16 ve VGG19, sırasıyla %96.7 ve %97 test doğruluklarına sahiptir ve daha verimli, kaliteli ve sürdürülebilir tohum üretiminin sağlanması için önemli bir araç olabilirler.

Keywords

Derin öğrenme, evrişimli sinir ağı, nohut sınıflandırma, transfer öğrenme

Thanks

T.C. Tarım ve Orman Bakanlığı Geçit Kuşağı Tarımsal Araştırmalar Enstitüsü Müdürlüğüne, Doğu Akdeniz Geçit Kuşağı Tarımsal Araştırmalar Enstitüsü Müdürlüğüne, Kahramanmaraş İl Tarım ve Orman Müdürlüğüne ve Kayseri Develi İlçe Tarım ve Orman Müdürlüğüne, nohut tohumlarının temini için teşekkür ederiz.

Chickpea Variety Classification Using Transfer Learning Techniques

Abstract

Seed purity is important for improving the efficiency of agricultural production and meeting product quality standards. This requires the agricultural seed industry, from producers to distributors/sellers, to focus more on seed purity. Therefore, the need for seed variety identification and classification methods has increased. The seed variety classification of chickpeas, one of the most produced edible legumes in the world, is examined in this study. 14 pre-trained deep learning models have been used for classification and their performances have been compared to determine the most successful model(s) for the relevant problem. The most successful models, VGG16 and VGG19, have test accuracies of 96.7% and 97%, respectively. Thus, they can be important tools for ensuring more efficient, high-quality, and sustainable seed production.

Keywords

Deep learning, convolutional neural network, chickpea classification, transfer learning

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