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

Entomolojide Yapay Sinir Ağlarının Kullanımı

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

• Alhady, S. S. N. ve Kai, X. Y. (2018). Butterfly species recognition using artificial neural network. In Hassan, M. (eds), Intelligent Manufacturing & Mechatronics. Lecture Notes in Mechanical Engineering (pp. 449-457). Springer. https://doi.org/10.1007/978-981-10-8788-2_40
• 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
• Bagnères, A. G., Rivière, G. ve Clément, J. L. (1998). Artificial neural network modeling of caste odor discrimination based on cuticular hydrocarbons in termites. Chemoecology, 8(4), 201-209. https://doi.org/10.1007/s000490050026
• Bauch, C. ve Rath, T. (2004). Prototype of a vision based system for measurements of white fly infestation. In International Conference on Sustainable Greenhouse Systems-Greensys 2004 (pp. 773-780). ISHS Acta Horticulturae 691. https://doi.org/10.17660/ActaHortic.2005.691.95
• Bianconi, A., Von Zuben, C. J., de Souza Serapião, A. B. ve Govone, J. S. (2010a). The use of artificial neural networks in analyzing the nutritional ecology of Chrysomya megacephala (F.)(Diptera: Calliphoridae), compared with a statistical model. Australian Journal of Entomology, 49(3), 201-212. https://doi.org/10.1111/j.1440-6055.2010.00754.x
• Bianconi, A., Zuben, C. J. V., Serapião, A. B. D. S. ve Govone, J. S. (2010b). Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala. Journal of Insect Science, 10(1), 58. https://doi.org/10.1673/031.010.5801
• Butcher, J. B., Moore, H. E., Day, C. R., Adam, C. D. ve Drijfhout, F. P. (2013). Artificial neural network analysis of hydrocarbon profiles for the aging of Lucilia sericata for post mortem interval estimation. Forensic Science International, 232(1-3), 25-31.https://doi.org/10.1016/j.forsciint.2013.06.018
• Carmo, D. D. G., Farias, E. D. S., Costa, T. L., Queiroz, E. A., Nascimento, M. ve Picanço, M. C. (2020). Instar determination of Blaptostethus pallescens (Hemiptera: Anthocoridae) using artificial neural networks. Annals of the Entomological Society of America, 113(1), 50-54. https://doi.org/10.1093/aesa/saz059
• Case, E., Shragai, T., Harrington, L., Ren, Y., Morreale, S. ve Erickson, D. (2020). Evaluation of unmanned aerial vehicles and neural networks for integrated mosquito management of Aedes albopictus (Diptera: Culicidae). Journal of Medical Entomology, 57(5), 1588-1595. https://doi.org/10.1093/jme/tjaa078
• Chesmore, D. (2004). Automated bioacoustic identification of species. Anais da Academia Brasileira de Ciências, 76(2), 436-440. https://doi.org/10.1590/S0001-37652004000200037
• Chon, T. S., Park, Y. S., Kim, J. M., Lee, B. Y., Chung, Y. J. ve Kim, Y. (2000). Use of an artificial neural network to predict population dynamics of the Forest–Pest pine needle gall midge (Diptera: Cecidomyiida). Environmental Entomology, 29(6), 1208-1215. https://doi.org/10.1603/0046-225X-29.6.1208
• Čirjak, D., Aleksi, I., Miklečić, I., Antolković, A. M., Vrtodušić, R., Viduka, A., ... ve Pajač Živković, I. (2022). Monitoring system for Leucoptera malifoliella (O. Costa, 1836) and its damage based on artificial neural networks. Agriculture, 13(1), 67. https://doi.org/10.3390/agriculture13010067
• Cletus, F., Baha, B. Y. ve Sarjiyus, O. (2022, November). Prediction of mosquito prevalence in a warm semi-arid climate using artificial neural network (ANN). In 2022 5th Information Technology for Education and Development (ITED) (s. 1-8). IEEE. https://doi.org/10.1109/ITED56637.2022.10051442
• Cocu, N., Harrington, R., Rounsevell, M. D. A., Worner, S. P., Hulle, M. ve Examine Project Participants. (2005). Geographical location, climate ,and land use influences on the phenology and numbers of the aphid, Myzus persicae, in Europe. Journal of Biogeography, 32(4), 615-632. https://doi.org/10.1111/j.1365-2699.2005.01190.x
• Çakır, F. S. (2018). Yapay Sinir Ağları Matlab Kodları ve Matlab Toolbox Çözümleri. Nobel Akademik Yayıncılık. Da Silva Motta, D., Badaró, R., Santos, A. ve Kirchner, F. (2018). Use of artificial intelligence on the control of vector-borne diseases. In Vectors and Vector-Borne Zoonotic Diseases. IntechOpen. https://doi.org/10.5772/intechopen.81671
• Damos, P., Tuells, J. ve Caballero, P. (2021). Soft computing of a medically important arthropod vector with autoregressive recurrent and focused time delay artificial neural networks. Insects, 12(6), 503. https://doi.org/10.3390/insects12060503
• De Los Reyes, A. M. M., Reyes, A. C. A., Torres, J. L., Padilla, D. A. ve Villaverde, J. (2016). Detection of Aedes Aegypti mosquito by digital image processing techniques and support vector machine. In 2016 IEEE Region 10 Conference (TENCON) (pp. 2342-2345). IEEE. https://doi.org/10.1109/TENCON.2016.7848448
• Demirsoy, A. (2003). Yaşamın Temel Kuralları, Omurgasızlar/Böcekler, Entomoloji. (2. Baskı, 2. Cilt, s. 119-122). Meteksan Matbaacılık.
• Do, M. T., Harp, J. M. ve Norris, K. C. (1999). A test of a pattern recognition system for identification of spiders. Bulletin of Entomological Research, 89(3), 217-224. https://doi.org/10.1017/S0007485399000334
• Elmas, Ç. (2016). Yapay Zekâ Uygulamaları: Yapay Sinir Ağı, Bulanık Mantık, Sinirsel Bulanık Mantık, Genetik Algoritma. Seçkin Yayıncılık.
• Entofito (2023). Entomoloji nedir? https://www.entofito.com/ adresinden 20 Nisan 2023 tarihinde alınmıştır.
• Espinoza, K., Valera, D. L., Torres, J. A., López, A. ve Molina-Aiz, F. D. (2016). Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture. Computers and Electronics in Agriculture, 127, 495-505. https://doi.org/10.1016/j.compag.2016.07.008
• Fedor, P., Malenovský, I., Vaňhara, J., Sierka, W. ve Havel, J. (2008). Thrips (Thysanoptera) identification using artificial neural networks. Bulletin of Entomological Research, 98(5), 437-447. https://doi.org/10.1017/S0007485308005750
• Fedor, P., Vaňhara, J., Havel, J., Malenovský, I. ve Spellerberg, I. (2009). Artificial intelligence in pest insect monitoring. Systematic Entomology, 34(2), 398-400. https://doi.org/10.1111/j.1365-3113.2008.00461.x
• Flórián, N., Jósvai, J. K., Tóth, Z., Gergócs, V., Sipőcz, L., Tóth, M. ve Dombos, M. (2023). Automatic Detection of Moths (Lepidoptera) with a Funnel Trap Prototype. Insects, 14(4), 381. https://doi.org/10.3390/insects14040381
• Hakimitabar, M., Shabaninejad, A. R., Saboori, A. ve Shams, M. H. (2017). Evaluation of Artificial Neural Network for determining distribution pattern of ascid family (Acari: Mesostigmata) in Damghan. Journal of Entomological Society of Iran, 37(3), 361-368. https://doi.org/10.22117/JESI.2017.116045.1149
• Hassoun, M. H. (1995). Fundamentals of Artificial Neural Networks. MIT press.
• Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall PTR.
• Haykin, S. (2010). Neural Networks and Learning Machines. Pearson.
• Howe, P. D., Bryant, S. R. ve Shreeve, T. G. (2007). Predicting body temperature and activity of adult Polyommatus icarus using neural network models under current and projected climate scenarios. Oecologia, 153, 857-869. https://doi.org/10.1007/s00442-007-0782-3
• Kang, S. H., Cho, J. H. ve Lee, S. H. (2014). Identification of butterfly based on their shapes when viewed from different angles using an artificial neural network. Journal of Asia-Pacific Entomology, 17(2), 143-149. https://doi.org/10.1016/j.aspen.2013.12.004
• Kang, S. H., Song, S. H. ve Lee, S. H. (2012). Identification of butterfly species with a single neural network system. Journal of Asia-Pacific Entomology, 15(3), 431-435. https://doi.org/10.1016/j.aspen.2012.03.006
• Kaya, Y. ve Kayci, L. (2014). Application of artificial neural network for automatic detection of butterfly species using color and texture features. The visual computer, 30(1), 71-79. https://doi.org/10.1007/s00371-013-0782-8
• Kaya, Y., Kayci, L. ve Uyar, M. (2015). Automatic identification of butterfly species based on local binary patterns and artificial neural network. Applied Soft Computing, 28, 132-137. https://doi.org/10.1016/j.asoc.2014.11.046
• Khalighifar, A., Komp, E., Ramsey, J. M., Gurgel-Gonçalves, R. ve Peterson, A. T. (2019). Deep learning algorithms improve automated identification of chagas disease vectors. Journal of Medical Entomology, 56(5), 1404–1410. https://doi.org/10.1093/jme/tjz065
• Kinney, A. C., Current, S. ve Lega, J. (2021). Aedes-AI: Neural network models of mosquito abundance. PLoS Computational Biology, 17(11), e1009467. https://doi.org/10.1371/journal.pcbi.1009467
• Lankin, G., Worner, S., Samarasinghe, S. ve Teulon, D. (2001). Can artificial Neural Network Systems be used for forecasting aphid flight patterns. New Zealand Plant Protection, 54, 188–192. https://doi.org/10.30843/nzpp.2001.54.3720
• Latif, M. S., Kazmi, R., Khan, N., Majeed, R., Ikram, S. ve Ali-Shahid, M. M. (2022). Pest prediction in rice using IoT and feed forward neural network. KSII Transactions on Internet and Information Systems (TIIS), 16(1), 133-152. https://doi.org/10.3837/tiis.2022.01.008
• 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
• Lorenz, C., Ferraudo, A. S. ve Suesdek, L. (2015). Artificial Neural Network applied as a methodology of mosquito species identification. Acta tropica, 152, 165-169. https://doi.org/10.1016/j.actatropica.2015.09.011
• 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
• Marcondes, C. B. ve Borges, P. S. (2000). Distinction of males of the Lutzomyia intermedia (Lutz ve Neiva, 1912) species complex by ratios between dimensions and by an artificial neural network (Diptera: Psychodidae, Phlebotominae). Memórias do Instituto Oswaldo Cruz, 95(5), 685-688. https://doi.org/10.1590/S0074-02762000000500012
• Mohamadi, R., Nejad, A. R. S., Alichi, M. ve Nejad, M. R. S. (2018). Evaluation of GMDH artificial neural network model to predict the spatial distribution of Coccinella septempunctata (Col.: Coccinellidae) in the alfalfa farm of Bajgah, Shiraz. Journal of Entomological Society of Iran, 38(3), 275-287. https://doi.org/10.22117/jesi.2018.116187.1154
• Moore, A. (1991). Artificial neural network trained to identify mosquitoes in flight. Journal of insect behavior, 4(3), 391-396. https://doi.org/10.1007/BF01048285
• Moore, H. E., Butcher, J. B., Adam, C. D., Day, C. R. ve Drijfhout, F. P. (2016). Age estimation of Calliphora (Diptera: Calliphoridae) larvae using cuticular hydrocarbon analysis and artificial neural networks. Forensic Science International, 268, 81-91. https://doi.org/10.1016/j.forsciint.2016.09.012
• 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
• Nauen, R. (2007). Insecticide resistance in disease vectors of public health importance. Pest Management Science, 63(7), 628-633. https://doi.org/10.1002/ps.1406
• Ouyang, T. H., Yang, E. C., Jiang, J. A. ve Lin, T. T. (2015). Mosquito vector monitoring system based on optical wingbeat classification. Computers and Electronics in Agriculture, 118, 47-55. https://doi.org/10.1016/j.compag.2015.08.021
• Öztemel, E. (2006). Yapay Sinir Ağları (2. Baskı). Papatya Yayıncılık.
• Popovic, D. (2020). Taxonomic identification of hoverfly specimens using neural network and gradient boosting machine techniques. Computational Ecology and Software, 10(3), 105-116. https://www.proquest.com/scholarly-journals/taxonomic-identification-hoverfly-specimens-using/docview/2756779299/se-2
• Samanta, R. K. ve Ghosh, I. (2012). Tea insect pests classification based on artificial neural networks. International Journal of Computer Engineering Science, 2(6), 336. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=2a6b390610ff9cbc17c4cc00b05c2ce62e3b5352
• Sanchez-Ortiz A, Fierro-Radilla A, Arista-Jalife A, Cedillo-Hernandez M, Nakano-Miyatake M, Robles-Camarillo D. ve Cuatepotzo-Jiménezet V. (2017). Mosquito larva classification method based on convolutional neural networks. In 2017 International Conference on Electronics, Communications, and Computers (CONIELECOMP) (s. 1-6). https://doi.org/10.1109/CONIELECOMP.2017.7891835
• Saruhan, I. (2012). Discrimination of the Palomena prasina L.(Heteroptera: Pentatomidae) nymph stages and sex using some morphological parameters by the multiple regression analysis. African Journal of Biotechnology, 11(9), 2365-2370. https://doi.org/10.5897/AJB11.3338
• Saruhan, I., Senyer, N., Ayvaz, T., Kayhan, G., Ergun, E., Odabas, M. S. ve Akca, İ. (2015). The estimation of adult and nymph stages of aphis fabae (Hemiptera: Aphididae) using artificial neural network. EntomologicalNewss, 125(1), 12-20. https://doi.org/10.3157/021.125.0104
• Shabaninejad, A., Tafaghodinia, B. ve Zandi-Sohani, N. (2017). Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran. Persian Journal of Acarology, 6(4). https://doi.org/10.22073/pja.v6i4.30295
• Shi, Z., Dang, H., Liu, Z. ve Zhou, X. (2020). Detection and identification of stored-grain insects using deep learning: A more effective neural network. IEEE Access, 8, 163703-163714. https://doi.org/10.1109/ACCESS.2020.3021830
• Silva, D. F., De Souza, V. M., Batista, G. E., Keogh, E. ve Ellis, D. P. (2013). Applying machine learning and audio analysis techniques to insect recognition in intelligent traps. In 2013 12th International Conference on Machine Learning and Applications, (s. 99-104). IEEE. https://ieeexplore.ieee.org/document/6784594
• Sinkins, S. P. ve Gould, F. (2006). Gene drive systems for insect disease vectors. Nature Reviews Genetics, 7(6), 427. https://doi.org/10.1038/nrg1870
• Starrett, S. K., Starrett, S. K., Najjar, Y., Adams, G. ve Hill, J. (1998). Modeling pesticide leaching from golf courses using artificial neural networks. Communications in Soil Science and Plant Analysis, 29(19-20), 3093-3106. https://doi.org/10.1080/00103629809370178
• Tan, A. N., Aylin, T. A. N. ve Susurluk, H. (2022). First application of two distinguishment techniques: Using Linear Discriminate Function method and Artificial Neural Networks approach according to the ovary types for some plant parasitic nematodes. Harran Tarım ve Gıda Bilimleri Dergisi, 26(1), 1-14. https://doi.org/10.29050/harranziraat.1025087
• Tonnang, H. E., Nedorezov, L. V., Owino, J. O., Ochanda, H. ve Löhr, B. (2010). Host–parasitoid population density prediction using artificial neural networks: diamondback moth and its natural enemies. Agricultural and Forest Entomology, 12(3), 233-242. https://doi.org/10.1111/j.1461-9563.2009.00466.x
• 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
• Wang, J., Lin, C., Ji, L. ve Liang, A. (2012). A new automatic identification system of insect images at the order level. Knowledge-Based Systems, 33, 102-110. https://doi.org/10.1016/j.knosys.2012.03.014
• Watts, M. J. ve Worner, S. P. (2008). Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species. Ecological Informatics, 3(1), 64-74. https://doi.org/10.1016/j.ecoinf.2007.06.004