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Journals / Tarım Bilimleri Dergisi / 2024 / Volume: 30 Issue: 4

Machine Learning-Based Grasshopper Species Classification using Neutrosophic Completed Local Binary Pattern

Nuh Alpaslan(BİNGÖL ÜNİVERSİTESİ)0000-0002-6828-755X
Mustafa İlçin(Bingöl Üniversitesi)0000-0002-2542-9503
Pages
—
Publication Date
2024-10-22
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It could have a big impact on data analysis, enabling more effective handling of global pest.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Locusts are seen as a major threat to the ecosystem because they devastate crops and contribute to thousands of tons food lost every year.
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Key Findings & Data:Our proposed system comprises a novel grasshopper species database of over 7.392 images for grasshopper species classification.
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Conclusion & Impact:It could have a big impact on data analysis, enabling more effective handling of global pest.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Neutrosophic set · Local binary pattern · locust

Abstract

Locusts are seen as a major threat to the ecosystem because they devastate crops and contribute to thousands of tons food lost every year. Numerous well-trained agents are needed for the efficient control of these insects. However, this is a challenging process. Grasshopper detection methods are being developed using traditional forecasting methods by expert entomologists. The maximum potential of these methods has not yet been completely realized. Hence the majority of work is still done manually. In this paper, a neutrosophic CLBP (completed local binary pattern) based grasshopper species classification framework is proposed. Our proposed system comprises a novel grasshopper species database of over 7.392 images for grasshopper species classification. The grasshopper image is first converted to a neutrosophic field. These discriminatory features are merged with rotation invariant LBP. Our proposed system could achieve up to 99.7% classification accuracy even while working with challenging datasets of wide image quality and size range. The proposed methodology involved diagnosing 11 species and subspecies. It demonstrates the impracticability of conventional diagnostic techniques in the later stages. It could have a big impact on data analysis, enabling more effective handling of global pest.

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Cite This Article

Alpaslan, N., İlçin, M. (2024). Machine Learning-Based Grasshopper Species Classification using Neutrosophic Completed Local Binary Pattern. Tarım Bilimleri Dergisi, Volume: 30 Issue: 4. https://doi.org/10.15832/ankutbd.1436890

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References (41)

  1. Alpaslan N (2022). Neutrosophic set based local binary pattern for texture classification. Expert Systems with Applications 209: 118350. - doi: 10.1016/J.ESWA.2022.118350
  2. Cheng X, Zhang Y, Chen Y, Wu Y & Yue Y (2017). Pest identification via deep residual learning in complex background. Computers and Electronics in Agriculture 141: 351–356
  3. Chudzik P, Mitchell A, Alkaseem M, Wu Y, Fang S, Hudaib T, Pearson S & Al-Diri B (2020). Mobile Real-Time Grasshopper Detection and Data Aggregation Framework. Scientific Reports 2020 10:1. 10: 1–10. - doi: 10.1038/s41598-020-57674-8
  4. Collett R A & Fisher D O (2017). Time-lapse camera trapping as an alternative to pitfall trapping for estimating activity of leaf litter arthropods. Ecology and Evolution 7: 7527–7533
  5. Ding W & Taylor G (2016). Automatic moth detection from trap images for pest management. Computers and Electronics in Agriculture 123: 17–28. - doi: 10.1016/J.COMPAG.2016.02.003
  6. El Khadiri I, Chahi A, El Merabet Y, Ruichek Y& Touahni R (2018). Local directional ternary pattern: A New texture descriptor for texture classification. Computer Vision and Image Understanding 169: 14–27
  7. El Khadiri I, Kas M, El Merabet Y, Ruichek Y& Touahni R (2018). Repulsive-and-attractive local binary gradient contours: New and efficient feature descriptors for texture classification. Information Sciences 467: 634–653. - doi: 10.1016/J.INS.2018.02.009
  8. El Merabet Y& Ruichek Y (2018). Local Concave-and-Convex Micro-Structure Patterns for texture classification. Pattern Recognition 76: 303–322. - doi: 10.1016/J.PATCOG.2017.11.005
  9. El Merabet Y, Ruichek Y& el idrissi A (2019). Attractive-and-repulsive center-symmetric local binary patterns for texture classification. Engineering Applications of Artificial Intelligence 78: 158–172
  10. Engel J, Hertzog L, Tiede J, Wagg C, Ebeling A, Briesen H, Weisser W W, Engel C, Hertzog L, Tiede J, Wagg C, Ebeling A, Briesen H& Weisser W W (2017). Pitfall trap sampling bias depends on body mass, temperature, and trap number: insights from an individual-based model. Ecosphere 8: e01790.
  11. FAO | Food and Agriculture Organization of the United Nations (2020). Desert Locust Upsurge.
  12. Gul E, Alpaslan N & Emiroglu M E (2021). Robust optimization of SVM hyper-parameters for spillway type selection. Ain Shams Engineering Journal 12: 2413–2423. - doi: 10.1016/J.ASEJ.2020.10.022
  13. Gullan P & Cranston P (2014). The insects: an outline of entomology. John Wiley & Sons.
  14. Guo Z, Zhang L& Zhang D (2010). A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing 19: 1657–1663
  15. Hansen O L P, Svenning J C, Olsen K, Dupont S, Garner B H, Iosifidis A, Price B W & Høye T T (2020). Species-level image classification with convolutional neural network enables insect identification from habitus images. Ecology and Evolution 10: 737–747
  16. He K, Zhang X, Ren S& Sun J (2015). Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, Vol. 2016-December
  17. Huang G, Liu Z, van der Maaten L & Weinberger K Q (2016). Densely Connected Convolutional Networks. In: 30th IEEE Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers Inc., Vol. 2017-January
  18. İlçin M (2019). Investigation of Orthoptera: Insecta Fauna of Useful, Harmful and Predator Species in the Batman Region (Turkey). Science Stays True Here" Biological and Chemical Research 6: 30–40
  19. İlçin M & Satar A (2018). On the Orthopteran Fauna (Insecta: Orthoptera) of Agricultural Regions of Batman Province (Turkey). Boletín de la Sociedad Entomológica Aragonesa (SEA) 62: 163-166
  20. İlçin M & Satar A (2020). Dociostaurus (Dociostaurus) maroccanus Thunberg, 1815 (Acrididae:Orthoptera) Türünün Sürü Oluşturma ve Bitkilere Zarar Durumunun Araştırılması. Turkish Journal of Nature and Science 9: 80–83
  21. İlçin M, Satar A & Balkaya A (2021). Remarks on the outbreak of Calliptamus italicus Linnaeus, 1758 (Acrididae: Orthoptera) in Bingöl province, Turkey., pp. 259–261. Retrieved from https://www.researchgate.net/publication/357480371
  22. Kasinathan T, Singaraju D & Uyyala SR (2021). Insect classification and detection in field crops using modern machine learning techniques. Information Processing in Agriculture 8: 446–457
  23. Liu L, Wang R, Xie C, Yang P, Wang F, Sudirman S & Liu W (2019). PestNet: An End-to-End Deep Learning Approach for Large-Scale Multi-Class Pest Detection and Classification. IEEE Access 7: 45301–45312
  24. Liu Z, Gao J, Yang G, Zhang H, He Y (2016). Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network. Scientific Reports 2016 6:1. 6: 1–12
  25. Martineau M, Conte D, Raveaux R, Arnault I, Munier D & Venturini G (2017). A survey on image-based insect classification. Pattern Recognition 65: 273–284
  26. Nanni L, Manfè A, Maguolo G, Lumini A & Brahnam S (2022). High performing ensemble of convolutional neural networks for insect pest image detection. Ecological Informatics 67: 101515
  27. Norouzzadeh M S, Nguyen A, Kosmala M, Swanson A, Palmer M S, Packer C & Clune J (2018). Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences of the United States of America 115: E5716–E5725.
  28. Ojala T, Pietikäinen M & Harwood D (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29: 51–59
  29. Ojala T, Pietikainen M & Maenpaa T (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24: 971–987
  30. Qin Y, Li Z, Zhao L, Fowler G, Fang Y (2013). The current and future potential geographical distribution of the Italian Locust, Calliptamus italicus (Linnaeus) (Orthoptera: Acrididae) in China. IFIP Advances in Information and Communication Technology 393 AICT: 290–298
  31. Sandler M, Howard A, Zhu M, Zhmoginov A & Chen L C (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society
  32. Sengur A, Budak U, Akbulut Y, Karabatak M & Tanyildizi E (2019). A survey on neutrosophic medical image segmentation. In: Neutrosophic Set in Medical Image Analysis
  33. Simonyan K & Zisserman A (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. In: 3rd International Conference on Learning Representations. International Conference on Learning Representations, ICLR
  34. Sinev S Y (2012). Coordinating the Traditional and Modern Approaches in the Systematics of Insects. Original Russian Text © S.Yu. Sinev. 92: 821–832
  35. Skvarla M J, Larson J L & Dowling A P G (2014). Pitfalls and preservatives: a review. The Journal of the Entomological Society of Ontario. 145 pp
  36. Sreedevi K, Meshram N & Shashank P R (2015). Insect Taxonomy—Basics to Barcoding. New Horizons in Insect Science: Towards Sustainable Pest Management pp. 3–12
  37. Valan M, Makonyi K, Maki A, Vondráček D, Vondráček V & Ronquist F (2019). Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks. Systematic Biology 68(6): 876-895
  38. Xia D, Chen P, Wang B, Zhang J & Xie C (2018). Insect Detection and Classification Based on an Improved Convolutional Neural Network. Sensors 2018, Vol. 18, Page 4169. 18: 4169
  39. Xie C, Wang R, Zhang J, Chen P, Dong W, Li R, Chen T & Chen H (2018). Multi-level learning features for automatic classification of field crop pests. Computers and Electronics in Agriculture 152: 233–241
  40. Zhang L, Lecoq M, Latchininsky A & Hunter D (2019). Locust and Grasshopper Management. https://doi.org/10.1146/annurev-ento-011118-112500 64: 15–34
  41. Zhang Z (2011). Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness. Magnolia press