Identifying Species of Mushrooms Through Mushrooms Images by Convolutional Neural Networks with Transfer Learning Method

Year 2021, Volume: 25 Issue: 1, 74 - 88, 20.04.2021

Bahadır Elmas

https://doi.org/10.19113/sdufenbed.818716

Abstract

Identification of fungi by convolutional neural networks; It is very important for the protection of vital mushrooms and for making sustainable management plans of mushroom resources. It is also a problem solution that can be useful in detecting poisonous mushrooms and for mushroom growers and collectors to identify mushrooms. Mushroom hats have many distinctive features such as rash, stain, scale, sash, groove, unique pattern and color. For this reason, it is thought that hat images will be more successful in defining according to Lamella, Ring, body and Volva images. In addition, the available datasets are insufficient or disorganized to train convolutional neural networks. In order to prove these two theses and contribute to the studies, three new datasets containing the images of 472 classes belonging to 18 families were created. The irregular dataset contains 148318 images. the dataset of the edited Hat, Lamella, Ring, Body and Volva images has 97450 images. Mushroom hat images created by editing consist of 65419 images. In the study, 6 convolutional neural networks were trained using the transfer learning method. The accuracy of the most successful network trained with the regulated mushroom hat dataset is 97.62 %. Although the number of images of this dataset is 44% less than the first dataset and 32% the second dataset. the success rate is 26.53% better than the first dataset and 14.5% the second dataset.

Keywords

Identifying species of mushrooms, Pre-Trained Networks, Transfer Learning

Evrişimli Sinir Ağları ile Mantar Görüntülerinden Mantar Türlerinin Transfer Öğrenme Yöntemiyle Tanımlanması

Abstract

Evrişimli sinir ağlarıyla mantarların tanımlanması; hayatî açıdan önemli mantarların koruma altına alınmasında ve mantar kaynaklarının sürdürülebilir yönetim planlarının yapılmasında oldukça önemlidir Ayrıca, zehirli mantarların tespit edilmesinde, mantar yetiştiricileri ile toplayıcıların mantarları tanımlamasında yararlı olabilecek bir problem çözümüdür. Mantar şapkaları döküntü, leke, pul, kuşak, yiv, özgün desen ve renk gibi çok sayıda ayırt edici özelliği sahiptirler. Bu nedenle şapka görüntülerinin Lamel, Yüzük, Sap ve Volva görüntülerine göre tanımlamaya daha çok katkısı olacağı düşünülmüştür. Bunun yanı sıra mevcut veri setleri evrişimli sinir ağları’nı eğitmek için yetersiz veya düzensizdir. Bu tezleri ispat etmek ve çalışmalara katkı sağlamak amacıyla, 18 aile ait 472 sınıfın görüntülerini içeren üç adet yeni veri seti oluşturulmuştur. Düzensiz veri seti 148318, düzenlenmiş Şapka, Lamel, Yüzük, Sap ve Volva görüntülerini içeren veri seti 97450 görüntü içermektedir. Düzenlenerek oluşturulmuş mantar şapka görüntüleri ise 65419 görüntüden oluşmaktadır. Çalışmada 6 evrişimli sinir ağı, transfer öğrenme yöntemi kullanılarak eğitilmiştir. Düzenlenmiş mantar şapka veri setiyle eğitilmiş en başarılı ağın doğruluk oranı %97.62’dir. Bu veri setinin görüntü sayısı, birinci veri setine göre %44, ikinci veri setine göre %32 daha az olmasına rağmen başarı oranı birinci veri setine göre %26.53, ikinci veri setine göre %14.5 daha iyidir.

Keywords

Mantar Türlerini Tanımlama, Önceden Eğitilmiş Ağlar, Transfer Öğrenme

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