Doğanın bir parçası olan bitkiler çevremize güzellik katmanın yanı sıra alternatif tıp gibi farklı sebep için de kullanılmaktadır. Bu gibi uzmanlık gerektiren durumlarda halk arasında yayılan yanlış bilgilerle zehirli bitkilerin şifalı olduğu düşünülerek kullanılması ölüme kadar gidebilecek sorunlara yol açmaktadır. Bu çalışmada yapay zeka teknikleri kullanılarak yaprak görüntülerindeki yaprak türlerinin belirlendiği bir sistem aracılığıyla bu sorunlara çözüm sağlanması amaçlanmaktadır. Son zamanlarda yaygın olarak kullanılan yapay zeka tekniklerinden biri olan evrişimli sinir ağı kullanılmıştır. Çok katmanlı yapısı, birçok parametreye sahip olması ve çok fazla ön işlem gerektirmeden öznitelik öğrenebilmesi, birçok çalışmada kullanılmasının nedenlerinden biridir. Bu çalışmada, sabit bir arka plana sahip yaprak görüntülerinden oluşan 5 farklı veri seti ile evrişimli sinir ağının eğitimi ayrı ayrı yapılmış ve bu eğitim sonucu parametrelerin eğitime olan etkisi incelenmiştir. Bu veri setlerinin birleştirilmesiyle elde edilen 270 türden oluşan birleştirilmiş bir veri seti oluşturulmuştur. Evrişimli sinir ağı ile genel amaçlı bir yaprak sınıflandırma modeli elde edilmiştir. Sınıflandırma işlemi ile elde edilen sonuçlar literatürdeki çalışmalar ile karşılaştırılmıştır.
Plants, which are part of nature, are also used for different reasons, such as alternative medicine, as well as to add beauty to our surroundings. In cases that require such expertise, the use of toxic plants with misinformation spread among the public is considered healable and leads to problems that can go to death. The aim of this study is to provide solutions to these problems through a system in which the types of leaves in leaf images are identified using artificial intelligence techniques. The evolutionary nerve network has been used, which is one of the techniques of artificial intelligence that has been widely used recently. Its multi-layer structure, having many parameters and being able to learn the specificity without requiring too much pre-processing, is one of the reasons for its use in many studies. In this study, the training of the evolutionary nervous network with 5 different data sets consisting of leaf images with a fixed background was done separately and the impact of the parameters on the training was studied as a result of this training. A combined data set consisting of 270 types obtained by the combination of these data sets has been created. With the evolutionary nerve network, a general-purpose leaf classification model has been obtained. The results obtained by the classification process are compared to the studies in literature.
Plants, which are a part of nature, are used for different reasons, such as alternative medicine as well as adding beauty to our environment. In such cases requiring expertise, the misinformation spread among the public and the use of poisonous plants considering that they are medicinal causes problems that can go up to death. In this study, it is aimed to solve these problems through a system that determines the species of leaves in leaf images using artificial intelligence techniques. Convolutional Neural Network (CNN), one of the most widely used artificial intelligence techniques, has been used recently. Its multi-layer structure, having many parameters and being able to learn features without requiring too much pre-processing is one of the reasons why it is used in many studies. In this study, the training of the convolutional neural network was carried out separately with 5 different data sets consisting of leaf images with a fixed background, and the effect of these training parameters on training was investigated. A combined data set consisting of 270 species obtained by combining these data sets was created. A general purpose leaf classification model is obtained with convolutional neural network. The results obtained by the classification process were compared with the studies in the literature.
Alan : Fen Bilimleri ve Matematik; Mühendislik
Dergi Türü : Ulusal
Benzer Makaleler | Yazar | # |
---|
Makale | Yazar | # |
---|