Patates Yaprağı Görüntülerinden Derin Öğrenme Tabanlı Hastalık Tespiti

Abstract

Bu tez çalışması, derin öğrenme yöntemleri kullanılarak patates görüntülerinden hastalık tespiti yapmayı amaçlamaktadır. Çalışmada, sağlıklı ve çeşitli patates hastalıklarına ait geniş ve kapsamlı bir görüntü veri seti kullanılmıştır. Farklı Evrişimli Sinir Ağı (CNN) mimarileri ve hibrit modelleri kullanılarak patates hastalıklarını tespit etmek için modeller geliştirilmiştir. Geliştirilen modeller farklı parametreler ve veri kümeleri kullanılarak eğitilmiş ve doğruluk, kesinlik gibi metrikler kullanılarak değerlendirilmiştir. Patates bitkilerinde görülen yaygın hastalıklar (geç yanıklık, erken yanıklık) tespit edilmiş ve görüntü ön işleme teknikleri kullanılarak modellerin performansı artırılmıştır. Bu çalışma, derin öğrenme yöntemlerinin patates hastalıklarının tespitinde etkili bir şekilde kullanılabileceğini göstermeyi ve bu alanda daha önce yapılan çalışmalara katkıda bulunmayı amaçlamaktadır. Çalışmada, dört farklı ResNet modeli ile görüntüler test edilmiş ve çeşitli performans metrikleriyle değerlendirilmiştir. Elde edilen bulguların, patates yetiştiriciliğinde hastalık yönetimi ve verimlilik artışı için önemli bilgiler sağlayabileceği düşünülmektedir. Yapay zeka ile görüntülerden hastalık tespiti yapılması tarım alanında yenilikler yapılmasına önayak olabileceği gibi makine-insan etkileşimine de artırıcı katkı sağlayabilir. Çalışmamız ResNet derin öğrenme modellerinin, görüntü çıkarımı alanında derin öğrenme modellerinin başarısını ve önemini vurgulamaktadır.

Keywords

• Evrişimsel Sinir Ağı (CNN)
• Derin Öğrenme
• Patates Hastalığı Tespiti
• ResNet.

Deep Learning based Disease Detection from Potato Leaf Images

Abstract

This study aims to detect diseases from potato images using deep learning methods. In the study, a large and comprehensive image dataset of healthy and various potato diseases was used. Models were developed to detect potato diseases using different Convolutional Neural Network (CNN) architectures and hybrid models. The developed models were trained using different parameters and datasets and evaluated using metrics such as accuracy and precision. Common diseases seen in potato plants (late blight, early blight) were detected and the performance of the models was increased using image preprocessing techniques. This study aims to show that deep learning methods can be used effectively in the detection of potato diseases and to contribute to previous studies in this field. In the study, images were tested with four different ResNet models and evaluated with various performance metrics. It is thought that the findings obtained can provide important information for disease management and productivity increase in potato cultivation. Disease detection from images with artificial intelligence can lead to innovations in the field of agriculture and can also contribute to machine-human interaction. Our work highlights the success and importance of ResNet deep learning models in the field of image extraction.

Keywords

• Convolutional Neural Network (CNN)
• Deep Learning
• Potato Disease Detection
• ResNet

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