Faster R-CNN ve YOLO Modelleri Kullanılarak Patates Yaprağı Hastalığı Tespiti

Yıl 2024, Cilt: 8 Sayı: 2, 144 - 150, 22.12.2024

Sara Medojević

Öz

Patates, toplam gıda üretimi açısından küresel olarak en önemli tarım ürünlerinden biridir ve küresel ekonomi üzerinde önemli bir etkiye sahiptir. Enfekte olmuş patates bitkileri, yapraklarında görülebilir semptomlar gösterir, bu da erken tespit, hastalık önleme ve enfekte olmamış bitkiler için riskin azaltılması sürecini büyük ölçüde kolaylaştırır. Akıllı tarım ve yeni ileri teknolojiler, gerçek zamanlı izleme ve analiz için farklı araçlar içermektedir. Patates yaprağı hastalığı tespiti için kullanılan modellerin çoğu, genellikle Bilgisayarlı Görü ve görüntü tanıma için uygun olan Derin Öğrenme mimarilerine, en yaygın olarak Konvolüsyonel Sinir Ağı (CNN) mimarisine dayanmaktadır. Bu makale, YOLOv11 Nesne Tespiti (Hızlı) modeli, YOLOv11s modeli ve Faster R-CNN X101-FPN modelinin performanslarını tasvir etmekte ve karşılaştırmaktadır. Bu modeller, Roboflow'da nesne tespiti için geliştirilmiş bir veri seti üzerinde eğitilmiştir. Bu veri seti, 1200 görüntü ve 1500 anotasyondan oluşmaktadır. Tek bir nesne, altı sınıftan biri olarak etiketlenmiştir: Zararlı, Bakteri, Mantar, Sağlıklı, Phytophthora ve Nematod. Performans metrikleri, bu modellerin aşırı eğitim süresine ihtiyaç duymadan itibarlı sonuçlar elde ettiğini ve bunları gerçek zamanlı izleme sistemleri için uygun hale getirdiğini göstermektedir. YOLOv11 Nesne Tespiti (Hızlı), YOLOv11s ve Faster R-CNN X101-FPN sırasıyla %95,1, %97,6 ve %92,62 mAP50 skorlarına ulaşmıştır.

Anahtar Kelimeler

YOLO modelleri, Faster R-CNN modeli, Roboflow, nesne tespiti, patates yaprağı hastalığı

Potato Leaf Disease Detection Using Faster R-CNN and YOLO Models

Öz

Potato is one of the most important food crops globally in terms of total food production, significantly impacting the global economy. Infected potato plants show visible symptoms on their leaves, which drastically simplifies the process of early detection, disease prevention, and minimizing the risk to uninfected plants. Smart farming and new advanced technologies incorporate different tools for real-time monitoring and analysis. Most of the models used for potato leaf disease detection are based on Deep Learning architectures, most commonly on Convolutional Neural Network (CNN) architecture, which is suitable for computer vision and image recognition. This paper depicts and compares the performances of the YOLOv11 Object Detection (Fast) model, YOLOv11s model, and Faster R-CNN X101-FPN model. These models were trained on a dataset developed for object detection in Roboflow. This dataset consists of 1200 images and 1500 annotations. A single object was labeled as one of the six classes: Pest, Bacteria, Fungi, Healthy, Phytophthora, and Nematode. Performance metrics show that these models achieve reputable results without excessive training time, making them suitable for real-time monitoring systems. YOLOv11 Object Detection (Fast), YOLOv11s, and Faster R-CNN X101-FPN achieved mAP50 scores of 95.1%, 97.6%, and 92.62%, respectively.

Anahtar Kelimeler

YOLO models, Faster R-CNN model, Roboflow, object detection, potato leaf disesase

Kaynakça

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