Use of Artificial Neural Networks in Entomology

Year 2023, , 131 - 145, 31.12.2023

Yeter Küçüktopcu , İslam Saruhan , Celal Tuncer , İzzet Akça

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

Abstract

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.

Keywords

Entomology, Artificial neural networks, Identification, Classification, Monitoring

Entomolojide Yapay Sinir Ağlarının Kullanımı

Abstract

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.

Keywords

Entomoloji, Yapay sinir ağları, Tanımlama, Sınıflandırma, İzleme

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