KaydedilenlerDerginizi ekleyin

Dergiler / Journal of Agricultural Sciences / 2026 / Volume: 32 Issue: 1

Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8

Yavuz Selim Şahin(SAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ)0009-0001-8634-0625
Nimet Sema Gençer(ULUDAĞ ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ)0009-0007-2435-2384
Hasan Şahin(BURSA TEKNİK ÜNİVERSİTESİ)0000-0002-8915-000X
Sayfa
—
Yayın Tarihi
2026-01-20
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Furthermore, future applications could extend this approach to other crops and pest species, validating the model’s versatility and supporting long-term farming sustainability.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Tomato (Solanum lycopersicum L.) is vital for global nutrition and economic stability, yet it is threatened by pests such as Tuta absoluta, Helicoverpa armigera, and Bemisia tabaci.
📊
Temel Bulgular & Veri:Effective pest management is crucial to prevent significant crop losses.
💡
Sonuç & Katkı:Furthermore, future applications could extend this approach to other crops and pest species, validating the model’s versatility and supporting long-term farming sustainability.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Solanum lycopersicum · Artificial intelligence · YOLO

Abstract

Tomato (Solanum lycopersicum L.) is vital for global nutrition and economic stability, yet it is threatened by pests such as Tuta absoluta, Helicoverpa armigera, and Bemisia tabaci. Effective pest management is crucial to prevent significant crop losses. Traditional pest detection methods relying on human observation are labor-intensive, time consuming, and prone to errors. In contrast, artificial intelligence (AI)based models such as YOLO provide timely and accurate pest identification, enhancing pest management practices. In this study, images captured throughout the tomato plant’s development, from seedling to fruit stage, were used for model training. The capabilities of the YOLOv8 model in detecting and segmenting tomato pests were evaluated. The results demonstrated significant improvements in both detection and segmentation tasks, with precision and recall reaching 98.91% and 98.98% for detection, and 97.47% and 98.81% for segmentation, respectively. These findings underscore the accuracy and robustness of the YOLOv8 model in monitoring diverse pest species, highlighting its potential to improve agricultural pest management practices. Although YOLO-based detectors have recently been tested on a limited set of pest species, comprehensive field-scale evaluations remain scarce. By assessing YOLOv8 across eleven pest taxa under commercial field conditions, this study delivers among the more comprehensive practice-oriented benchmarks to date for multi-species pest monitoring. This research suggests that integrating AI models like YOLOv8 into pest monitoring systems can contribute to more efficient and sustainable agricultural practices by minimizing human error and labor demands. Furthermore, future applications could extend this approach to other crops and pest species, validating the model’s versatility and supporting long-term farming sustainability.

🔖

Bu Makaleye Atıf Yap

Şahin, Y. S., Gençer, N. S., Şahin, H. (2026). Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8. Journal of Agricultural Sciences, Volume: 32 Issue: 1. https://doi.org/10.15832/ankutbd.1681258

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (58)

  1. Ahmad I, Yang Y, Yao Y, Wang H, Hassan M, Cheng X, Wu Y & Zhang Y (2022). Deep learning-based detector YOLOv5 for identifying insect pests. Applied Sciences 12(19):10167. https://doi.org/10.3390/app121910167
  2. Arfan M, Sumardi S & Huboyo H (2023). Advancing workplace safety: A deep learning approach for personal protective equipment detection using single shot detector. In: Proceedings of the 2023 International Workshop on Artificial Intelligence and Image Processing (IWAIIP), 127–132. IEEE. https://doi.org/10.1109/IWAIIP59925.2023.10385441
  3. Arockia V, Mary E & Radha P (2023). Pest detection using image denoising and cascaded UNet segmentation for pest images. Tuijin Jishu/Journal of Propulsion Technology 44(4):1359–1371. https://doi.org/10.52783/tjjpt.v44.i4.1040
  4. Arun RA & Umamaheswari S (2022). Effective and efficient multi-crop pest detection based on deep learning object detection models. Journal of Intelligent & Fuzzy Systems 43(4):5185-5203. https://doi.org/10.3233/JIFS-220595
  5. Ashbrook A R, Mikaelyan A & Schal C (2022). Comparative efficacy of a fungal entomopathogen with a broad host range against two human-associated pests. Insects 13(9):774. https://doi.org/10.3390/insects13090774
  6. Atalan A, Şahin H & Atalan Y A (2022). Integration of machine learning algorithms and discrete-event simulation for the cost of healthcare resources. Healthcare 10:1920. https://doi.org/10.3390/healthcare10101920
  7. Barbedo J G A (2016). A review on the main challenges in automatic plant disease identification based on visible-range images. Biosystems Engineering 144:52–60. https://doi.org/10.1016/j.biosystemseng.2016.01.017
  8. Baştürk F & Şahin H (2022). Makine öğrenmesi sınıflandırma algoritmalarının karşılaştırması: Metinden dil tanıma örneği. Electronic Letters on Science and Engineering 18(2):68–78.
  9. Bellout A, Zarboubi M, Dliou A, Latif R, & Saddik A (2024). Advanced YOLO models for real-time detection of tomato leaf diseases. Mathematical Modeling and Computing 11(4):1198–1210. https://doi.org/10.23939/mmc2024.04.1198
  10. Blackman R L & Eastop V F (2000). Aphids on the World’s Crops: An Identification and Information Guide (2nd edn). John Wiley & Sons Ltd, Chichester, UK, 466 pp.
  11. Bütüner A K, Şahin Y S, Erdinç A, Erdoğan H & Lewıs E (2024). Enhancing pest detection: Assessing Tuta absoluta (Lepidoptera: Gelechiidae) damage intensity in field images through advanced machine learning. Journal of Agricultural Sciences 30(1): 99-107. https://doi.org/10.15832/ankutbd.1308406
  12. Chen C J, Huang Y Y, Li Y S, Chang C Y & Huang Y M (2020). An AIoT based smart agricultural system for pests detection. IEEE Access 8:180750–180761. https://doi.org/10.1109/ACCESS.2020.3024891
  13. Choi Y, Kim N, Paudel B & Kim H (2022). Strawberry pests and diseases detection technique optimized for symptoms using deep learning algorithm. Journal of Bio-Environment Control 31(3):255–260. https://doi.org/10.12791/ksbec.2022.31.3.255
  14. Desneux N, Wajnberg E, Wyckhuys KAG (2010). Biological invasion of European tomato crops by Tuta absoluta: Ecology, geographic expansion and prospects for biological control. Journal of Pest Science 83:197–215. https://doi.org/10.1007/s10340-010-0321-6
  15. Dong S, Zhang J, Wang F & Wang X (2022). YOLO-pest: A real-time multi-class crop pest detection model. In: Proceedings of the International Conference on Computer Application and Information Security (ICCAIS 2021), Vol. 12260, 12–18. SPIE, Bellingham, WA, USA. https://doi.org/10.1117/12.2637467
  16. Everingham M, Eslami S A, Van Gool L, Williams C K, Winn J, & Zisserman A (2015). The pascal visual object classes challenge: A retrospective. International journal of computer vision 111:98-136.
  17. Garzia G, Siscaro G, Biondi A & Zappalà L (2012). Tuta absoluta, a South American pest of tomato now in the EPPO region: Biology, distribution and damage. EPPO Bulletin 42:205–210. https://doi.org/10.1111/epp.2556
  18. Golatkar N, Krithika & Hemalatha N (2020). Applications of deep learning in agriculture (pest detection). Redshine Archive. https://doi.org/10.25215/8119070682.36
  19. Güven Ö & Şahin H (2022). Predictive maintenance based on machine learning in public transportation vehicles. Mühendislik Bilimleri ve Araştırmaları Dergisi 4(1):89–98. the IEEE Conference
  20. Hariharan B, Arbeláez P, Girshick R & Malik J (2015). Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of on Computer Vision and Pattern Recognition (CVPR 2015), 447–456. https://doi.org/10.1109/CVPR.2015.7298642
  21. Hoebeke E R & Carter M E (2003). Halyomorpha halys (Stål) (Heteroptera: Pentatomidae): A new adventive pest of some crops in North America. Proceedings of the Entomological Society of Washington 105(1):225–237.
  22. Jelali M (2024). Deep learning networks-based tomato disease and pest detection: a first review of research studies using real field datasets. Frontiers in Plant Science 15. https://doi.org/10.3389/fpls.2024.1493322 the
  23. Jensen M B, Nasrollahi K & Moeslund T B (2017). Evaluating state-of-the-art object detector on challenging traffic light data. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2017), 9–15. https://doi.org/10.1109/CVPRW.2017.122
  24. Kang G, Hou L, Zhao Z & Lang B (2023). Research on the application of convolutional neural network based on YOLO algorithm in pest small target detection. In: Proceedings of the 2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), 131–135. IEEE.
  25. Khalid S, Oqaibi H, Aqib M & Hafeez Y (2023). Small pests detection in field crops using deep learning object detection. Sustainability 15(8):6815. https://doi.org/10.3390/su15086815
  26. Kumbasaroğlu H, Özçelik A & Çelikkol P (2021). Growing tomato under protected cultivation conditions: Overall effects on productivity, nutritional yield, and pest incidences. Crops 1(2):97–110. https://doi.org/10.3390/crops1020010
  27. Leybourne D J, Musa N & Yang P (2024). Can artificial intelligence be integrated into pest monitoring schemes to help achieve sustainable agriculture? An entomological, management and computational perspective. Agricultural and Forest Entomology (in press). https://doi.org/10.1111/afe.12630
  28. Li M, Zhang Z, Lei L, Wang X & Guo X (2020). Agricultural greenhouse detection in high-resolution satellite images based on convolutional neural networks: Comparison of Faster R-CNN, YOLOv3, and SSD. Sensors 20(17):4938. https://doi.org/10.3390/s20174938
  29. Li D, Li H Y, Zhang J R, Wu Y J, Zhao S X, Liu S S & Pan L L (2023). Plant resistance against whitefly and its engineering. Frontiers in Plant Science 14:1232735. https://doi.org/10.3389/fpls.2023.1232735
  30. Liu J & Wang X (2020). Tomato diseases and pests detection based on improved YOLO V3 convolutional neural network. Frontiers in Plant Science 11:898. https://doi.org/10.3389/fpls.2020.00898
  31. Lyu Z, Jin H, Zhen T, Sun F & Xu H (2021). Small object recognition algorithm of grain pests based on SSD feature fusion. IEEE Access 9:43202-43213. https://doi.org/10.1109/ACCESS.2021.3066510
  32. Jiao L, Xie C, Chen P, Du J, Li R & Zhang J (2022). Adaptive feature fusion pyramid network for multi-classes agricultural pest detection. Computers and electronics in agriculture 195:106827. https://doi.org/10.1016/j.compag.2022.106827
  33. Mirhaji H, Soleymani M, Asakereh A & Mehdizadeh S A (2021). Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions. Computers and Electronics in Agriculture 191:106533. https://doi.org/10.1016/j.compag.2021.106533
  34. Oliveira L, Durão A, Fontes J, Roja I S & Tavares J (2017). Potential of Trichogramma achaeae (Hymenoptera: Trichogrammatidae) in biological control of Tuta absoluta (Lepidoptera: Gelechiidae) in Azorean greenhouse tomato crops. Journal of Economic Entomology 110:2010–2015. https://doi.org/10.1093/jee/tox197
  35. Pereyra P C & Sánchez N (2006). Effect of two solanaceous plants on developmental and population parameters of the tomato leaf miner, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Neotropical Entomology 35(5):671–676. https://doi.org/10.1590/S1519-566X2006000500016
  36. Raikov A & Abrosimov V (2022). Artificial intelligence and robots in agriculture. In: Proceedings of the 2022 15th International Conference on Management of Large-Scale System Development (MLSD), 1–5. IEEE.
  37. Rajamohanan R & Latha B C (2023). An optimized YOLO v5 model for tomato leaf disease classification with field dataset. Engineering, Technology & Applied Science Research 13(6):12033–12038. https://doi.org/10.48084/etasr.6377
  38. Ranger C M, Singh A P, Frantz J M, Cañas L, Locke J C, Reding M E & Vorsa N (2009). Influence of silicon on resistance of Zinnia elegans to Myzus persicae (Hemiptera: Aphididae). Environmental Entomology 38(1):129–136. https://doi.org/10.1603/022.038.0116
  39. Redmon J & Farhadi A (2018). YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767. https://doi.org/10.48550/arXiv.1804.02767
  40. Redmon J, Divvala S, Girshick R & Farhadi A (2016). You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 779–788. https://doi.org/10.1109/CVPR.2016.345
  41. Rubanga D, Loyani L, Richard M & Shimada S (2020). A deep learning approach for determining effects of Tuta absoluta in tomato plants. arXiv preprint arXiv:2004.04023. https://doi.org/10.48550/arXiv.2004.04023
  42. Şahin H & Topal B (2016). The effect of the use of information technologies in businesses on cost and financial performance. International Journal of Engineering Innovations and Research 5(6):394–402.
  43. Şahin H & Topal B (2018). Impact of information technology on business performance: Integrated structural equation modeling and artificial neural network approach. Scientia Iranica 25(3):1272–1280.
  44. Şahin H, Güntürkün R & Hız O (2020). Design and application of PLC controlled robotic arm choosing objects according to their color. Electronic Letters on Science and Engineering 16(2):52–62.
  45. Şahin Y S, Erdinç A, Bütüner A K & Erdoğan H (2023). Detection of Tuta absoluta larvae and their damages in tomatoes with deep learning-based algorithm. International Journal of Next-Generation Computing 14(3):87–98. https://doi.org/10.47164/ijngc.v14i3.1287
  46. Shaltiel-Harpaz L, Gerling D, Graph S et al. (2015). Control of the tomato leafminer, Tuta absoluta (Lepidoptera: Gelechiidae), in open-field tomatoes by indigenous natural enemies occurring in Israel. Journal of Economic Entomology 109:120–131. https://doi.org/10.1093/jee/tov309
  47. Sylla S, Brévault T, Bal A et al. (2017). Rapid spread of the tomato leafminer, Tuta absoluta (Lepidoptera: Gelechiidae), an invasive pest in Sub-Saharan Africa. Entomologia Generalis 36:269–283. https://doi.org/10.1127/ENTOMOLOGIA/2017/0453 Vilar-Andreu M, García L, García-Sánchez A, Asorey-Cacheda R & García-Haro J (2024). Enhancing Precision Agriculture Pest Control: A Generalized Deep Learning Approach With YOLOv8-Based Insect Detection. IEEE Access 12:84420-84434. https://doi.org/10.1109/ACCESS.2024.3413979
  48. Wang J, Huang W & Zhang Q (2022). Agricultural light-trapped pest detection methods under unbalanced data. In: Proceedings of the 2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT), 1–5. IEEE.
  49. Wen C, Chen H, Ma Z, Zhang T, Yang C, Su H & Chen H (2022). Pest-YOLO: A model for large-scale multi-class dense and tiny pest detection and counting. Frontiers in Plant Science 13:973985. https://doi.org/10.3389/fpls.2022.973985
  50. Xiang Q, Huang X, Huang Z, Chen X, Cheng J & Tang X (2023). Yolo-Pest: An Insect Pest Object Detection Algorithm via CAC3 Module. Sensors 23(6):3221. https://doi.org/10.3390/s23063221
  51. Xu Y, Xing L & Zhou Y (2023). Research on lightweight target detection algorithm of farmland insect pests based on YOLO-PPLCBot. Journal of Electronic Imaging 32(4):043008. https://doi.org/10.1117/1.JEI.32.4.043008
  52. Yang S, Li J, Li Y, Nie J, Qiao Y & Ercisli S (2024). Fuzzy EfficientDet: An approach for precise detection of larch infestation severity in UAV imagery under dynamic environmental conditions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17:8810-8822. https://doi.org/10.1109/JSTARS.2024.3389289
  53. Yılmaz M, Şahin H & Yıldız A (2021). Sectoral application analysis of studies made with deep learning models. Electronic Letters on Science and Engineering 17(2):126–140.
  54. Yong A, Sun C & Jun T (2020). Research on recognition model of crop diseases and insect pests based on deep learning in harsh environments. IEEE Access 8:171686–171693. https://doi.org/10.1109/ACCESS.2020.3025325
  55. Yuan J, Wang L, Li Q, Yu L & Fang K (2024). Deep learning‑based agricultural pest and disease recognition. Chinese Computer Sciences Review. https://doi.org/10.48014/ccsr.20231015001
  56. Yuan W, Lan L, Xu J, Sun T, Wang X, Wang Q, Wang B (2025). Smart Agricultural Pest Detection Using I-YOLOv10-SC: An Improved Object Detection Framework. Agronomy 15(1):221. https://doi.org/10.3390/agronomy15010221
  57. Yue G, Yaqiu L, Tong N, Lina L, Limin A, Zhengyuan W & Mingyu D (2024). GLU-YOLOv8: An Improved Pest and Disease Target Detection Algorithm Based on YOLOv8. Forests 15(9):1486. https://doi.org/10.3390/f15091486 Zhang L, Ding G, Li C & Li D (2023a). DCF-Yolov8: An improved algorithm for aggregating low-level features to detect agricultural pests and diseases. Agronomy 13(8):2012. https://doi.org/10.3390/agronomy13082012
  58. Zhang S, Zhang C, Park D & Yoon S (2023b). Editorial: Machine learning and artificial intelligence for smart agriculture, volume II. Frontiers in Plant Science 14:1166209. https://doi.org/10.3389/fpls.2023.1166209