Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model

Ahmet Namlı; Didem Ölçer

doi:10.18016/ksutarimdoga.vi.1486797

EN TR

Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model

Abstract

The classification of mushroom species presents significant ecologic and health-related challenges; advancement in classification techniques is required to gain reliable identifications. This study aims to explain a methodology that was devised and evaluated in the development of a novel, lightweight Convolutional Neural Network (CNN) designed specifically for the task of mushroom classification. The paper provides a custom CNN model that is computationally cost-effective and capable of high-precision classification, fit for real-time usage. Hence, the proposed model was evaluated on this dataset of curated mushroom images with traditional classifiers and state-of-the-art CNN architectures, such as EfficientNet-B7, ResNet50, InceptionV3, and MobileNetV2. The custom model is depth-wise separations engineered in such a way that while they reduce the computational load, they don't compromise the effectiveness of the model. The custom model achieved a test score of 0.68, which is moderate compared to more established models such as EfficientNet-B7 or ResNet50. This approach helps the model function effectively even on platforms having low computational resources. A comprehensive evaluation reveals that a custom CNN has reasonable accuracy in the identification of different mushroom species vis-à-vis existing models, but also significantly lightens the classification process.

Keywords

CNN Algoritmalarının Mantar Sınıflandırmada Önerilen Hafif CNN Modeli ile Karşılaştırmalı Analizi

Öz

Mantar türlerinin sınıflandırılması, ekolojik ve sağlıkla ilgili önemli zorluklar ortaya koymaktadır; güvenilir tanımlamalar elde etmek için sınıflandırma tekniklerinde ilerleme kaydedilmesi gerekmektedir. Bu çalışma, mantar sınıflandırma görevi için özel olarak tasarlanmış yeni, hafif bir Evrişimsel Sinir Ağının (CNN) geliştirilmesi üzere tasarlanan ve değerlendirilen bir metodolojiyi açıklamayı amaçlamaktadır. Makale, hesaplama açısından uygun maliyetli ve yüksek hassasiyetli sınıflandırma yapabilen, gerçek zamanlı kullanıma uygun özel bir CNN modeli sunmaktadır. Bu nedenle, önerilen model, geleneksel sınıflandırıcılar ve EfficientNet-B7, ResNet50, InceptionV3 ve MobileNetV2 gibi son teknoloji CNN mimarileri ile mantar görüntülerinden oluşan bu veri kümesi üzerinde değerlendirilmiştir. Özel modelin, hesaplama karmaşıklığını azaltırken modelin etkinliğinden ve yeteneğinden ödün vermeyecek şekilde tasarlanmasına özen gösterilmiştir. Özel model, EfficientNet-B7 veya ResNet50 gibi daha yerleşik modellerle karşılaştırıldığında orta düzeyde bir değer olan 0,68'lik bir test puanı elde etti. Bu yaklaşım, modelin düşük hesaplama kaynaklarına sahip platformlarda bile etkili bir şekilde çalışmasına yardımcı olur. Kapsamlı bir değerlendirme, tasarlanan CNN'in yalnızca mevcut modellere kıyasla farklı mantar türlerinin tanımlanmasında makul bir doğruluğa sahip olduğunu değil, aynı zamanda sınıflandırma sürecini de önemli ölçüde hafiflettiğini ortaya koymaktadır.

Anahtar Kelimeler

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Tarım Ekonomisi (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Ahmet Namlı
0000-0002-4649-3299
Türkiye

Didem Ölçer ^*
0000-0001-7736-1021
Türkiye

Erken Görünüm Tarihi

15 Eylül 2024

Yayımlanma Tarihi

25 Aralık 2024

Gönderilme Tarihi

20 Mayıs 2024

Kabul Tarihi

14 Ağustos 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 27 Sayı: Ek Sayı 1 (Suppl 1)

DOI

https://doi.org/10.18016/ksutarimdoga.vi.1486797

IZ

https://izlik.org/JA43XS44FY

Kaynak Göster

RIS / Bibtex

APA

Namlı, A., & Ölçer, D. (2024). Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 27(Ek Sayı 1 (Suppl 1), 243-253. https://doi.org/10.18016/ksutarimdoga.vi.1486797

Cited By

Prediction of Egg Weight in Japanese Quails (Coturnix coturnix japonica) with Internal Quality Traits Using Machine Learning Algorithms

Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi

https://doi.org/10.18016/ksutarimdoga.vi.1695149

Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model

Abstract

Keywords

CNN Algoritmalarının Mantar Sınıflandırmada Önerilen Hafif CNN Modeli ile Karşılaştırmalı Analizi

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Erken Görünüm Tarihi

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

Prediction of Egg Weight in Japanese Quails (Coturnix coturnix japonica) with Internal Quality Traits Using Machine Learning Algorithms

e-ISSN: 2619-9149