Detection of Tuta absoluta larvae and their damages in tomatoes with deep learning-based algorithm


Yavuz Selim Şahin
Atilla Erdinç
Alperen Kaan Bütüner
Hilal Erdoğan


Plant pests cause significant economic losses to the agricultural sector. Tuta absoluta is one of the most important pests of the tomato plant, which has a high financial return. Accurate and rapid identification of tomato plant pests is essential to increase sustainable agricultural productivity. Computer vision and machine learning techniques such as deep learning and especially Convolutional Neural Networks (CNN) have effectively identified various plant pests. Training CNN-based algorithms that allow us to classify and identify objects can enable faster and more accurate pest detection than human observation. We used YOLOv5 (You Look Only Once), a CNN-based object detection algorithm. One thousand two hundred photos of tomato leaves infested by the T. absoluta pest were edited to train the YOLOv5 algorithm. Images include T. absoluta larvae and galleries created in leaves by these larvae. Experimental results showed that using the YOLOv5 algorithm could categorize tomato plant leaves correctly and detect T. absoluta larvae, galleries with 80% and 70-90% accuracy (mAP), respectively. The research is promising that deep learning-based object identification algorithms can be significantly effective in detecting agricultural pests early and preventing excessive use of pesticides.


How to Cite
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).


  1. Altas¸, Z., ¨Ozg¨uven, M. M., and Dilmac¸, M. 2021. G¨or¨unt¨u ˙ I¸sleme teknikleri ile ba˘g yaprak uyuzu hasarının belirlenmesi. Gaziosmanpa¸sa Bilimsel Ara¸stırma Dergisi Vol.10, 77–87.
  2. Bengio, Y. and LeCun, Y. 2007. Scaling learning algorithms towards ai. Large-scale kernel machines Vol.34, pp.1–41. DOI:
  3. Erdo˘gan, H., B¨ut¨uner, A. K., and S¸ahin, Y. S. 2023. Detection of cucurbit powdery mildew, sphaerotheca fuliginea (schlech.) polacci by thermal imaging in field conditions. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol.23, pp.189–192.
  4. Fu, L., Feng, Y., Wu, J., Liu, Z., Gao, F., Majeed, Y., Al-Mallahi, A., Zhang, Q., Li, R., and Cui, Y. 2021. Fast and accurate detection of kiwifruit in orchard using improved yolov3-tiny model. Precision Agriculture Vol.22, pp.754–776. DOI:
  5. He, Y., Zhou, Z., Tian, L., Liu, Y., and Luo, X. 2020. Brown rice planthopper (nilaparvata lugens stal) detection based on deep learning. Precision Agriculture Vol.21, pp.1385–1402. DOI:
  6. Lino, P. B. 2010. La resistencia a insecticidas en tuta absoluta (meyrick). Phytoma Espa˜na: La revista profesional de sanidad vegetal Vol.217, pp.103–106.
  7. Liu, B., Zhang, Y., He, D., and Li, Y. 2017. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry Vol.10, pp.11. DOI:
  8. Mkonyi, L., Rubanga, D., Richard, M., Zekeya, N., Sawahiko, S., Maiseli, B., and Machuve, D. 2020. Early identification of tuta absoluta in tomato plants using deep learning. Scientific African Vol.10, pp.E00590. DOI:
  9. Nayana, B. and Kalleshwaraswamy, C. 2015. Biology and external morphology of invasive tomato leaf miner, tuta absoluta (meyrick) (lepidoptera: Gelechiidae). Pest Management In Horticultural Ecosystems Vol.21, pp.169–174.
  10. Pathak, T. and Stoddard, S. 2018. Climate change effects on the processing tomato growing season in california using growing degree day model. Modeling Earth Systems And Environment Vol.4, pp.765–775. DOI:
  11. Rubanga, D., Loyani, L., Richard, M., and Shimada, S. 2020. A deep learning approach for determining effects of tuta absoluta in tomato plants. Arxiv Vol.Arxiv:2004, pp.04023.
  12. Savary, S. and Willocquet, L. 2014. Simulation modelling in botanical epidemiology and crop loss analysis. Plant Health Instructor, pp.147.
  13. Schreinemachers, P., Simmons, E., and Wopereis, M. 2018. Tapping the economic and nutritional power of vegetables. Global Food Security vol.16, pp.36–45. DOI:
  14. van der Blom, J., Robledo, A., Torres, S., and Sanchez, J. A. 2009. Consequences of the wide-scale implementation of biological control in greenhouse horticulture in almeria, spain. Biological Control Vol.11.
  15. Viggiani, G., Filella, F., Delrio, G., Ramassini, W., and Foxi, C. 2009. Tuta absoluta, a new lepidoptera now reported in italy. Informatore Agrario vol.65, pp.66–68.
  16. Xie, Y., Jiang, J., Bao, H., Zhai, P., Zhao, Y., Zhou, X., and Jiang, G. 2022. Recognition of big mammal species in airborne thermal imaging based on yolo v5 algorithm. Integrative Zoology vol.00, pp.1–20. DOI:
  17. Zekeya, N., Chacha, M., Ndakidemi, P., Materu, C., Chidege, M., and Mbega, E. 2016. Tomato leafminer (tuta absoluta meyrick 1917): A threat to tomato production in africa. Journal Of Agriculture And Ecology Research International vol.10, pp.1–10. DOI: