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Cochineal Colony Detection in Cactus Pear: A Deep Learning Approach

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Optimization, Learning Algorithms and Applications (OL2A 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2280))

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Abstract

Object detection is a fundamental task in computer vision, playing a crucial role in various applications such as surveillance, autonomous driving, and agriculture. In agricultural contexts, object detection techniques are essential for assessing plant health and implementing targeted interventions. In this paper, we introduce a novel methodology for the detection of cochineal colonies of Dactylopius opuntiae in cactus pear, which aims to estimate the degree of infestation and facilitate precise treatment strategies. Leveraging recent advancements in deep learning, we present a new dataset specifically curated for colony cochineal detection in cactus pear. We evaluate the performance of three state-of-the-art deep learning models, namely YOLOV7, YOLOV8, and YOLO-NAS, using our dataset. Through rigorous experimentation and comparative analysis, we identify YOLOV8 as the most effective model for colony cochineal detection in cactus pear. The proposed approach not only offers accurate colony detection but also provides valuable insights for implementing precise treatment measures, thereby contributing to the efficient management of plant infestations.

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References

  1. Diwan, T., Anirudh, G., Tembhurne, J.V.: Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimed. Tools Appl. 82(6), 9243–9275 (2023)

    Article  Google Scholar 

  2. Vijayakumar, A., Vairavasundaram, S.: YOLO-based object detection models: a review and its applications. Multimed. Tools Appl. 1–40 (2024)

    Google Scholar 

  3. Kaur, R., Singh, S.: A comprehensive review of object detection with deep learning. Digit. Signal Process. 132, 103812 (2023)

    Article  Google Scholar 

  4. Shubo, X., Zhang, M., Song, W., Mei, H., He, Q., Liotta, A.: A systematic review and analysis of deep learning-based underwater object detection. Neurocomputing 527, 204–232 (2023)

    Article  Google Scholar 

  5. Khalid, S., Oqaibi, H.M., Aqib, M., Hafeez, Y.: Small pests detection in field crops using deep learning object detection. Sustainability 15(8), 6815 (2023)

    Article  Google Scholar 

  6. Xiang, Q., Huang, X., Huang, Z., Chen, X., Cheng, J., Tang, X.: YOLO-pest: an insect pest object detection algorithm via CAC3 module. Sensors 23(6), 3221 (2023)

    Article  Google Scholar 

  7. Teixeira, A.C., Ribeiro, J., Morais, R., Sousa, J.J., Cunha, A.: A systematic review on automatic insect detection using deep learning. Agriculture 13(3), 713 (2023)

    Article  Google Scholar 

  8. El Aalaoui, M., Sbaghi, M.: Potential of parasitoids to control diaspis echinocacti (bouché)(hemiptera: Diaspididae) on opuntia spp. cactus pear. Egypt. J. Biol. Pest Control 33(1), 57 (2023)

    Article  Google Scholar 

  9. El Fakhouri, K., et al.: Isolation, identification and pathogenicity of local entomopathogenic bacteria as biological control agents against the wild cochineal Dactylopius opuntiae (Cockerell) on cactus pear in Morocco. Sci. Rep. 13(1), 21647 (2023)

    Article  Google Scholar 

  10. Vanegas-Rico, J.M., Lomelí-Flores, J.R., Rodríguez-Leyva, E., Mora-Aguilera, G., Valdez, J.M.: Enemigos naturales de dactylopius opuntiae (cockerell) en opuntia ficus-indica (l.) miller en el centro de méxico. Acta zoológica mexicana 26(2), 415–433 (2010)

    Article  Google Scholar 

  11. Ramdani, C., et al.: Chemical composition and insecticidal potential of six essential oils from morocco against dactylopius opuntiae (cockerell) under field and laboratory conditions. Insects 12(11), 1007 (2021)

    Article  Google Scholar 

  12. Ramdani, C., et al.: Entomopathogenic fungi as biological control agents of dactylopius opuntiae (hemiptera: Dactylopiidae) under laboratory and greenhouse conditions. Frontiers in Sustain. Food Syst. 6, 997254 (2022)

    Article  Google Scholar 

  13. Naboulsi, I., et al.: Insecticidal activities of atriplex halimus l., salvia rosmarinus spenn. and cuminum cyminum l. against dactylopius opuntiae (cockerell) under laboratory and greenhouse conditions. Insects 13(10), 930 (2022)

    Article  Google Scholar 

  14. Naboulsi, I., et al.: Chemical profiling of artemisia herba-alba, cuminum cyminum, cinnamomum camphora, and salvia rosmarinus essential oils and assessment of their insecticidal potential to control the wild cochineal dactylopius opuntiae (cockerell). Crop Protect. 171, 106286 (2023)

    Article  Google Scholar 

  15. Park, Y.-H., Choi, S.H., Kwon, Y.-J., Kwon, S.-W., Kang, Y.J., Jun, T.-H.: Detection of soybean insect pest and a forecasting platform using deep learning with unmanned ground vehicles. Agronomy 13(2), 477 (2023)

    Article  Google Scholar 

  16. Liu, B., Jia, Y., Liu, L.: Skip DETR: end-to-end skip connection model for small object detection in forestry pest dataset. Front. Plant Sci. 14, 1219474 (2023)

    Article  Google Scholar 

  17. Costa, D., Silva, C., Costa, J., Ribeiro, B.: Enhancing pest detection models through improved annotations. In: EPIA Conference on Artificial Intelligence, pp. 364–375. Springer (2023)

    Google Scholar 

  18. Kaur, J., Singh, W.: A systematic review of object detection from images using deep learning. Multimed. Tools Appl. 83(4), 12253–12338 (2024)

    Article  Google Scholar 

  19. Ge, L., Singh, P., Sadhu, A.: Advanced deep learning framework for underwater object detection with multibeam forward-looking sonar. Struct. Health Monit.14759217241235637 (2024)

    Google Scholar 

  20. Ai, Y., Song, R., Huang, C., Cui, C., Tian, B., Chen, L.: A real-time road boundary detection approach in surface mine based on meta random forest. IEEE Trans. Intell. Veh. (2023)

    Google Scholar 

  21. Shukla, R.K., Tiwari, A.K., Jha, A.K., et al.: An efficient approach of face detection and prediction of drowsiness using SVM. Math. Probl. Eng. 2023 (2023)

    Google Scholar 

  22. Xiong, S., Li, B., Zhu, S.: DCGNN: a single-stage 3D object detection network based on density clustering and graph neural network. Complex Intell.Sys. 9(3), 3399–3408 (2023)

    Article  Google Scholar 

  23. Rahman, A., Lu, Y., Wang, H.: Performance evaluation of deep learning object detectors for weed detection for cotton. Smart Agric. Technol. 3, 100126 (2023)

    Article  Google Scholar 

  24. Song, P., Li, P., Dai, L., Wang, T., Chen, Z.: Boosting R-CNN: Reweighting R-CNN samples by RPN’s error for underwater object detection. Neurocomputing 530, 150–164 (2023)

    Article  Google Scholar 

  25. Nandhini, T.J., Thinakaran, K.: Object detection algorithm based on multi-scaled convolutional neural networks. In: 2023 3rd International conference on Artificial Intelligence and Signal Processing (AISP), pp. 1–5. IEEE (2023)

    Google Scholar 

  26. Taye, M.M.: Theoretical understanding of convolutional neural network: concepts, architectures, applications, future directions. Computation 11(3), 52 (2023)

    Article  Google Scholar 

  27. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  28. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)

    Google Scholar 

  29. Jiang, P., Ergu, D., Liu, F., Cai, Y., Ma, B.: A review of yolo algorithm developments. Procedia Comput. Sci. 199, 1066–1073 (2022)

    Article  Google Scholar 

  30. Li, Y., Mao, H., Girshick, R., He, K.: Exploring plain vision transformer backbones for object detection. In: European Conference on Computer Vision, pp. 280–296. Springer (2022)

    Google Scholar 

  31. Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023)

    Google Scholar 

  32. Jocher, G., et al.: Ultralytics/YOLOv5: v5. 0-YOLOv5-p6 1280 models, AWS, supervise. ly and YouTube integrations. Zenodo (2021)

    Google Scholar 

  33. Yung, N.D.T., Wong, W.K., Juwono, F.H., Sim, Z.A.: Safety helmet detection using deep learning: implementation and comparative study using YOLOv5, YOLOv6, and YOLOv7. In: 2022 International Conference on Green Energy, Computing and Sustainable Technology (GECOST), pp. 164–170. IEEE (2022)

    Google Scholar 

  34. Silva, S.Q.: Proposta para avaliação do controle biológico da cochonilha diaspis echinocacti (bouché, 1833) (homoptera, diaspididae) da palma forrageira em pernambuco. Mater’s thesis, Universidade Federal Rural de Pernambuco, Recife, Brazil (1991)

    Google Scholar 

  35. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  36. Zhao, H., Zhang, H., Zhao, Y.: YOLOv7-sea: Object detection of maritime UAV images based on improved YOLOv7. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 233–238 (2023)

    Google Scholar 

  37. Rangga Gelar Guntara: Pemanfaatan google colab untuk aplikasi pendeteksian masker wajah menggunakan algoritma deep learning YOLOv7. J. Teknol. Sist. Informasi Bisnis 5(1), 55–60 (2023)

    Article  Google Scholar 

  38. Terven, J., Cordova-Esparza, D.: A comprehensive review of YOLO architectures in computer vision: from YOLOv1 to YOLOv8 and YOLO-NAS. arXiv preprint arXiv:2304.00501 (2023)

  39. Hussain, M.: YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines 11(7), 677 (2023)

    Article  Google Scholar 

  40. Talaat, F.M., ZainEldin, H.: An improved fire detection approach based on YOLO-v8 for smart cities. Neural Comput. Appl. 35(28), 20939–20954 (2023)

    Article  Google Scholar 

  41. Zhang, Y., et al.: Deep learning models for waterfowl detection and classification in aerial images. Information 15(3), 157 (2024)

    Article  Google Scholar 

  42. Casas, E., Ramos, L., Bendek, E., Rivas-Echeverría, F.: Assessing the effectiveness of YOLO architectures for smoke and wildfire detection. IEEE Access (2023)

    Google Scholar 

  43. Padilla, R., Netto, S.L., Da Silva, E.A.B.: A survey on performance metrics for object-detection algorithms. In: 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 237–242. IEEE (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Digital Innovation Center of Excellence, Mohammed VI Polytechnic University, Ben Guerir, Morocco

    Wiam Salhi, Rafiq El Alami & Hafid Griguer

  2. AgroBioSciences Program, College of Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University, Ben Guerir, Morocco

    Karim El Fakhouri & Mustapha El Bouhssini

Authors
  1. Wiam Salhi
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  2. Karim El Fakhouri
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  3. Mustapha El Bouhssini
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  4. Rafiq El Alami
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  5. Hafid Griguer
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Corresponding author

Correspondence to Wiam Salhi .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  3. Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  5. Department of Electrical Engineering, Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  7. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

  8. Dept ISAATC, Universidad de La Laguna, San Cristóbal de La Laguna, Spain

    Santiago T. Álvarez

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Salhi, W., El Fakhouri, K., El Bouhssini, M., El Alami, R., Griguer, H. (2024). Cochineal Colony Detection in Cactus Pear: A Deep Learning Approach. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2024. Communications in Computer and Information Science, vol 2280. Springer, Cham. https://doi.org/10.1007/978-3-031-77426-3_15

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