Kümes Çevresindeki Tilki Tespiti için Derin Öğrenme ve Yolov8 Modellerinin Karşılaştırılması

Year 2024, Volume: 5 Issue: 2, 76 - 90, 20.12.2024

Murat Erhan Çimen

https://doi.org/10.58769/joinssr.1498561

Abstract

Yüzyıllardır tarım ve hayvancılıkla uğraşan insanoğlu, sürekli olarak kendi tarım arazilerini ve hayvanlarını takip etmek, bakımını yapmak ve sürdürmek zorundadır. Bu da sürekli emek ve zaman gerektirir. Bu çalışmanın amacı ve özgünlüğü, kümes hayvancılığı yapan bireylerin kümeslerinde yaşayan tavuk, kaz, ördek, hindi gibi hayvanları tehdit eden, onlara zarar veren veya kaçıran tilkilerin tespit edilmesidir. Bu sayede çiftçi o an kümeste olmasa dahi çiftçinin maddi ve manevi kayıplarının önüne geçilmiş olacaktır. Bu amaca ulaşmak için birçok görsel toplanarak veri seti oluşturuldu. Toplanan veri seti tilkinin kümeste olup olmamasına göre sınıflandırıldı. Daha sonra kümesteki tilki varlığını tespit etmek amacıyla transfer öğrenmede gerçekleştirilecek şekilde DenseNet, MobileNet, ResNet50, VGG16, VGG19, Xception ve Yolov8 mimarilerinin çıkışlarına ince ayar yapıldı. Daha sonra modeller eğitilerek performansları kayıp, doğruluk, hassasiyet ve F1 gibi performans metrikleri açısından karşılaştırıldı. Sonuçlara göre Yolov8 mimarileri genel olarak en iyi performansı sergilemiştir.

Keywords

Kümes hayvacılığı, Derin Öğrenme, Yolov8

Comparison of Deep Learning and Yolov8 Models for Fox Detection Around the Henhouse

Abstract

Human beings, who have been engaged in agriculture and animal husbandry for centuries, have to constantly track, take care and maintain their own agricultural lands and animals. This requires constant labor and time. The aim and originality of this study is to identify foxes that kidnap animals such as chickens, geese, ducks and turkeys that live in the coops of individuals engaged in poultry farming. In this way, even if the farmer is not in the henhouse at that moment, material and moral losses to the farmers will be prevented. To achieve this purpose, many images were collected to form dataset. The collected dataset was classified according to whether the fox was in the henhouse or not. Then, the outputs of DenseNet, MobileNet, ResNet50, VGG16, VGG19, Xception and Yolov8 architectures were fine tuned to be performed in transfer learning to detect existence of a fox in the henhouse. Then, the models were trained, and their performances were compared in terms of performance metrics such as loss, accuracy, precision and F1. In the results, Yolov8 architectures generally have demonstrated the best performances.

Keywords

Poultry Farming, Deep Learning, Yolov8

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