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Use of Artificial Neural Networks in Entomology

Yıl 2023, Cilt: 13 Sayı: 2, 131 - 145, 31.12.2023

Yeter Küçüktopcu İslam Saruhan Celal Tuncer İzzet Akça

https://doi.org/10.54370/ordubtd.1286217

Öz

In recent years, artificial neural networks (ANN) have become an important tool in entomology, and their use has rapidly increased. Entomologists are taking advantage of the possibilities offered by ANN in various fields. These applications range from predicting insect species and monitoring insect populations to identifying pests and modeling insect behavior. The ability to quickly and accurately analyze large datasets resulting from observations and measurements, especially in agriculture, provides a significant advantage for entomologists in developing insect control strategies. This review confirms that ANN is a valuable and effective tool in entomology and provides an overview of its potential future applications. However, the development and application of ANN technology require sustained effort. During ANN applications, attention should be given to the training process, and it's essential to acknowledge that each new study contributes to neural network training. As a result, entomologists should focus on exploring the potential of ANN further and work towards implementing this innovative method on a larger scale in entomology. By doing so, it will be possible to gain deeper insights into the nature of insects, develop environmentally-friendly control strategies, and establish more sustainable and efficient production processes in agricultural areas.

Anahtar Kelimeler

Entomology Artificial neural networks Identification Classification Monitoring

Kaynakça

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Entomolojide Yapay Sinir Ağlarının Kullanımı

Yıl 2023, Cilt: 13 Sayı: 2, 131 - 145, 31.12.2023

Yeter Küçüktopcu İslam Saruhan Celal Tuncer İzzet Akça

https://doi.org/10.54370/ordubtd.1286217

Öz

Son yıllarda, entomoloji alanında yapay sinir ağları (YSA) önemli bir araç haline gelmiş ve kullanımı hızla artmıştır. Entomologlar, YSA'nın sunduğu olanaklardan pek çok alanda yararlanmaktadır. YSA'nın kullanımı; böcek türlerinin tahmininden başlayarak, böcek popülasyonlarının takibine, zararlı böceklerin belirlenmesine ve böcek davranışlarının modellemesine kadar çeşitli uygulamalara olanak tanımaktadır. Özellikle tarım alanlarında yapılan gözlemler ve ölçümler sonucunda elde edilen büyük veri kümelerinin hızlı ve hassas bir şekilde analiz edilmesi, böceklerle mücadele stratejilerinin geliştirilmesinde entomologlara önemli bir avantaj sağlamaktadır. Bu derleme, YSA'nın entomolojide kullanılabilir ve etkili bir araç olduğunu göstermekte ve YSA'nın gelecekteki uygulama potansiyeline genel bir bakış sunmaktadır. Ancak, YSA teknolojisinin geliştirilmesi ve uygulanması süreklilik arz eden bir çaba gerektirmektedir. YSA uygulamalarında eğitim sürecine özen gösterilmeli ve her yeni çalışmanın sinir ağı eğitimine katkı sağlayacağı unutulmamalıdır. Bu nedenle, entomologlar YSA'nın potansiyelini daha fazla keşfetmeye odaklanmalı ve bu yenilikçi yöntemi entomolojide daha geniş ölçekte kullanmaya yönelik çalışmalar yapmalıdır. Böylece; böceklerin doğası hakkında daha derin bilgilere ulaşmak, çevre dostu mücadele stratejileri geliştirmek, tarım alanlarında daha sürdürülebilir ve verimli üretim süreçleri geçirmek mümkün olacaktır. YSA'nın entomoloji alanında ilerlemesi, hem bilimsel araştırmalara hem de tarım sektörüne önemli katkılar sağlayacaktır.

Anahtar Kelimeler

Entomoloji Yapay sinir ağları Tanımlama Sınıflandırma İzleme

Kaynakça

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  • Altay, O. ve Özgen, I. (2021). Predicting the LD50 values of two different vinegars whose insecticidal effect was determined by the spraying method against Tribolium confusum Jacquelin du val (Coleoptera: Tenebrionidae) using different artificial neural network models. Zoological and Entomological Letters, 1(2), 39-47. https://www.zoologicaljournal.com/article/16/1-2-4-122.pdf
  • Ayob, M. Z. ve Chesmore, E. D. (2013). Probabilistic Neural Network for the Automated Identification of the Harlequin Ladybird (Harmonia Axyridis). In Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds), Multi-disciplinary Trends in Artificial Intelligence (pp. 25-35). Springer. https://doi.org/10.1007/978-3-642-44949-9_3
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  • Lee, S. H. ve Kang, S. H. (2016). Performance enhancement of the branch length similarity entropy descriptor for shape recognition by introducing critical points. Journal of the Korean Physical Society, 69(7), 1254-1262. https://doi.org/10.3938/jkps.69.1254
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  • Luquin, M. F. H., Santacruz, E. V., Morales, R. A. L., Vázquez, C. N. ve Zúñiga, M. G. (2017). Development of intelligent tools for recognizing cockroaches in the forensic entomology context. In 2017 Intelligent Systems Conference (IntelliSys) (pp. 1117-1121). IEEE. htpss://doi.org/10.1109/IntelliSys.2017.8324269
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  • Moore, H. E., Butcher, J. B., Day, C. R. ve Drijfhout, F. P. (2017). Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species. Forensic Science International, 280, 233-244. https://doi.org/10.1016/j.forsciint.2017.10.001
  • Mukundarajan, H., Hol, F. J. H., Castillo, E. A., Newby, C. ve Prakash, M. (2017). Using mobile phones as acoustic sensors for high-throughput mosquito surveillance. Elife, 6, e27854. https://doi.org/10.7554/eLife.27854
  • Nabet, C., Chaline, A., Franetich, J. F., Brossas, J. Y., Shahmirian, N., Silvie, O., ... ve Piarroux, R. (2020). Prediction of malaria transmission drivers in Anopheles mosquitoes using artificial intelligence coupled to MALDI-TOF mass spectrometry. Scientific Reports, 10(1), 1-13. https://doi.org/10.1038/s41598-020-68272-z
  • Naeim Amini, S., Golizadeh, A., Tafaghodinia, B., Razmjou, J., Abbasipour, H. ve Shaabaninejad, A. (2021). Evaluation of artificial neural network MLP optimized with genetic algorithm in estimating and predicting R0 and rm of greenhouse whitefly Trialeurodes vaporariorum (Hemiptera: Aleyrodoidae) according to some characteristics of host plants under greenhouse conditions. Journal of Entomological Society of Iran, 41(1), 55-72. https://doi.org/10.22117/JESI.2021.354385.1415
  • Narava, R., Kumar, S. R., Jaba, J., Kumar, A., Rao, R., Rao, S., Mishra, S. P. ve Kukanur, V. (2022). Development of Temporal Model for Forecasting of Helicoverpa armigera (Noctuidae: Lepidopetra) Using Arima and Artificial Neural Networks. Journal of Insect Science, 22(3), 2. https://doi.org/10.1093/jisesa/ieac019
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  • Öztemel, E. (2006). Yapay Sinir Ağları (2. Baskı). Papatya Yayıncılık.
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  • Vanhara, J., Havel, J. ve Fedor, P. (2010). Artificial neural networks (ANN) in entomology. https://www.researchgate.net/publication/295253809_Artificial_Neural_Networks_ANN_in_entomology adresinden 20 Nisan 2023 tarihinde alınmıştır.
  • Vaňhara, J., Muráriková, N., Malenovský, I. ve Havel, J. (2007). Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae). Biologia, 62(4), 462-469. https://doi.org/10.2478/s11756-007-0089-1
  • Von Zuben, C. J., Stangenhaus, G. ve Godoy, W. A. C. (2000). Larval competition in Chrysomya megacephala (F.)(Diptera: Calliphoridae): effects of different levels of larval aggregation on estimates of weight, fecundity, and reproductive investment. Revista Brasileira de Biologia, 60, 195-203. https://doi.org/10.1590/S0034-71082000000200002
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Toplam 71 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ziraat, Veterinerlik ve Gıda Bilimleri
Bölüm Araştırma Makaleleri
Yazarlar

Yeter Küçüktopcu 0000-0002-2104-5764

İslam Saruhan 0000-0003-0229-9627

Celal Tuncer 0000-0002-9014-8003

İzzet Akça 0000-0001-9617-8820

Erken Görünüm Tarihi 29 Aralık 2023
Yayımlanma Tarihi 31 Aralık 2023
Gönderilme Tarihi 20 Nisan 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 13 Sayı: 2

Kaynak Göster

APA Küçüktopcu, Y., Saruhan, İ., Tuncer, C., Akça, İ. (2023). Entomolojide Yapay Sinir Ağlarının Kullanımı. Ordu Üniversitesi Bilim Ve Teknoloji Dergisi, 13(2), 131-145. https://doi.org/10.54370/ordubtd.1286217
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