PRE-TRAINED NETWORK BASED DEEP NETWORK MODEL FOR CLASSIFICATION OF LEAF DISEASES
نویسندگان
چکیده
Bitkiye zarar veren hastalıkların erken teşhisi, kimyasal tarım ilaçlarının tüketimini azaltmak, mali olarak tasarruf etmek ve çevreye verilen kirliliği engelleyebilmek için oldukça önemlidir. Elma ağaç yapraklarında oluşan herhangi bir hastalık durumunda, belirtilerini aşamada tespit edebilmek çiftçiler uzman personelinden destek almak zorunda kalmaktadır. Bu durum çiftçilere büyük maliyet oluşturmaktadır. Bahsedilen problemi çözebilmek adına scab, rust her ikisinin arada kullanılabileceği çoklu gruplarını sınıflandırabilmek Konvolüsyonel Sinir Ağı (CNN) yöntemi tabanlı derin öğrenme modeli geliştirilmiştir. Önerilen yaklaşım popüler transfer öğrenim teknikleri olen DenseNet201, MobileNetV2, ResNet50V2, ResNet101V2, ResNet152V2 algoritmalarını giriş katmanı kullanan CNN katmanlarının birleşiminden oluşmaktadır. Geliştirilen yöntem farklı seviyelerde aydınlatma, gürültü, arka planı homojen olmama durumlarını içeren zorluk seviyesi yüksek veri seti üzerinde test edilmiştir. Test işlemlerinde önerilen yöntemle sınıflandırma doğruluk oranı %97 değerine ulaşılmıştır.
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ژورنال
عنوان ژورنال: Ad?yaman Üniversitesi mühendislik bilimleri dergisi
سال: 2021
ISSN: ['2149-0309']
DOI: https://doi.org/10.54365/adyumbd.988049