An expert system for honeybee species identification and information retrieval

Year 2025, Volume: 18 Issue: 1, 1 - 12

Swati Shilaskar , Shripad Bhatlawande , Shafaque Sheikh , Sahil Salve , Tayyab Shaikh

https://doi.org/10.46309/biodicon.2025.1515641

Abstract

Detecting honeybee species is important for ecological and agricultural research, as it helps researchers understand their behavior, population, movement pattern, and pollination habits. The paper proposes a honey bee Identification system categorizing five subspecies: Apis Cerena Indica, Apis Mellifera, Apis Florea, Apis Dorsata, and Trigona. Input images of honeybees are preprocessed to improve quality and eliminate any noise. Data augmentation methods are used to increase the dataset size, ensuring effective model training. The VGG16 architecture, known for its success in image recognition tasks, is utilized to identify important features from the dataset. Further, Rectified Linear Unit (ReLU) and Softmax layers are added, increasing the model's efficiency. The support Vector Machine model is trained to classify 5 classes of honey bees. After training the model, accurate predictions of different honeybee species with high levels of precision and recall are made. These results prove that the system effectively identifies 5 subspecies of honeybees. This system performs exceptionally well in species classification, providing advancements in ecological and agricultural studies, by implementing VGG16 and SVM.

Keywords

Ecosystem conservation, Honeybees, Computer vision, VGG16, SVM.

Bal arısı türlerinin tespiti ve bilgi erişimi için uzman sistem

Abstract

Bal arısı türlerinin tespit edilmesi, araştırmacıların davranışlarını, popülasyonlarını, hareket şekillerini ve tozlaşma alışkanlıklarını anlamalarına yardımcı olduğundan ekolojik ve tarımsal araştırmalar için önemlidir. Makale, beş alt türü kategorize eden bir bal arısı Tanımlama sistemi önermektedir: Apis Cerena Indica, Apis Mellifera, Apis Florea, Apis Dorsata ve Trigona. Bal arılarının girdi görüntüleri, kaliteyi artırmak ve gürültüyü ortadan kaldırmak için önceden işlenir. Veri kümesi boyutunu artırmak ve etkili model eğitimi sağlamak için veri büyütme yöntemleri kullanılır. Görüntü tanıma görevlerindeki başarısıyla bilinen VGG16 mimarisi, veri kümesinden önemli özelliklerin tanımlanmasında kullanılıyor. Ayrıca, Düzeltilmiş Doğrusal Birim (ReLU) ve Softmax katmanları eklenerek modelin verimliliği artırılıyor. Destek Vektör Makinesi modeli, 5 sınıf bal arısını sınıflandırmak için eğitilmiştir. Model eğitildikten sonra farklı bal arısı türlerine ilişkin yüksek hassasiyet ve geri çağırma ile doğru tahminler yapılır. Bu sonuçlar, sistemin bal arılarının 5 alt türünü etkili bir şekilde tanımladığını kanıtlıyor. Bu sistem, VGG16 ve SVM'yi uygulayarak ekolojik ve tarımsal çalışmalarda ilerlemeler sağlayarak tür sınıflandırmasında olağanüstü iyi performans gösterir.

Keywords

Ekosistemin korunması, Bal arıları, Bilgisayarla görme, VGG16, SVM.

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