A Novel Brazilian Dataset for Automatic Detection of Psyllid Attacksin Guava

Iago Magalhães de Mesquita; Larissa Ferreira Rodrigues Moreira; Rhuan da Silva Nunes; Iális Cavalcante de Paula Júnior

doi:10.22456/2175-2745.143544

Authors

Iago Magalhães de Mesquita Universidade Federal do Ceará (UFC) https://orcid.org/0000-0003-4347-7979
Larissa Ferreira Rodrigues Moreira Universidade Federal de Viçosa (UFV) https://orcid.org/0000-0001-8947-9182
Rhuan da Silva Nunes Universidade Federal do Ceará (UFC) https://orcid.org/0009-0001-2625-8937
Iális Cavalcante de Paula Júnior Universidade Federal do Ceará (UFC) https://orcid.org/0000-0002-2374-4817

DOI:

https://doi.org/10.22456/2175-2745.143544

Keywords:

Psyllids, Deep Learning, Guava Trees, Object Detection

Abstract

Guava, a fruit native to America, plays a crucial role in the Brazilian economy, with production reaching 564,000 tons by 2022. It is widely used in culinary dishes and herbal remedies, and is exported to numerous countries. However, guava is also susceptible to pests and diseases. Among these, Psyllid Triozoda Limbata is particularly problematic, attacking young leaves and leading to reduced fruit yields and impaired plant development. To address this issue and assist producers in detecting pest infestations, this study introduces a novel dataset comprising images from Brazil. By leveraging Artificial Intelligence and Computer Vision techniques, this dataset provides a foundation for developing pest detection systems. We evaluated various object detection architectures including different versions of YOLO and Vision Transformer (ViT). The experiments indicated that YOLO V5 achieved promising results, with a precision of 93.14%. Moreover, the availability of this dataset opens new opportunities for enhancing pest management strategies, improving crop protection, and increasing the overall productivity in the guava industry.

Downloads

Download data is not yet available.

References

SANTOS, T. P. D. et al. Natural parasitism in Triozoida limbata (Enderlein, 1918) (Hemiptera: Triozidae) in a semi-arid region of Brazil. Revista Caatinga, Universidade Federal Rural do Semi-Árido, v. 35, n. 1, p. 239–242, Jan. 2022. ISSN 1983-2125. Disponível em: ⟨https://doi.org/10.1590/1983-21252022v35n125rc⟩.

OLIVEIRA, G. S. d. et al. Participatory survey of citrus in the “Recôncavo Baiano region” with emphasis on the threat of Huanglongbing. Revista Brasileira de Fruticultura, Sociedade Brasileira de Fruticultura, v. 43, n. 4, p. e-168, 2021. ISSN 0100-2945. Disponível em: ⟨https://doi.org/10.1590/0100-29452021168⟩.

IBGE. Produção Agropecuária. 2023. Disponível em: ⟨https://www.ibge.gov.br/explica/producao-agropecuaria/goiaba/br⟩.

RENAN, Q.; SAULO, F. Controle do psilídeo da goiabeira (Triozoida limbata) com o uso de medicamento homeopático. Revista Científica Faesa, v. 18, n. 1, p. 162–177, jul. 2022. ISSN 2316-7327. Disponível em: ⟨http://revista.faesa.br/revista/index.php/Faesa/issue/archive⟩.

MOREIRA, R. et al. AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics. Internet of Things, v. 19, p. 100570, 2022. ISSN 2542-6605. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S2542660522000634⟩.

THAI, H.-T.; LE, K.-H.; NGUYEN, N. L.-T. Towards sustainable agriculture: A lightweight hybrid model and cloud-based collection of datasets for efficient leaf disease detection. Future Generation Computer Systems, v. 148, p. 488–500, 2023. ISSN 0167-739X. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S0167739X23002388⟩.

BARBEDO, J. G. A. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computers and Electronics in Agriculture, v. 153, p. 46–53, 2018. ISSN 0168-1699. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S0168169918304617⟩.

RAJBONGSHI, A. et al. A comprehensive guava leaves and fruits dataset for guava disease recognition. Data in Brief, v. 42, p. 108174, 2022. ISSN 2352-3409. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S235234092200378X⟩.

PATHMANABAN, P.; GNANAVEL, B.; ANANDAN, S. S. Comprehensive guava fruit data set: Digital and thermal images for analysis and classification. Data in Brief, v. 50, p. 109486, 2023. ISSN 2352-3409. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S2352340923005863⟩.

MATTIHALLI, C. et al. Plant leaf diseases detection and auto-medicine. Internet of Things, v. 1-2, p. 67–73, 2018. ISSN 2542-6605. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S2542660518300453⟩.

SHETTY, K. U. et al. Plant Disease Detection for Guava and Mango using YOLO and Faster R-CNN. In: 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). Gwalior, India: IEEE, 2024. v. 2, p. 1–6.

MESQUITA, I. M. de et al. Psyllid Attacks in Guava. Zenodo, 2024. Disponível em: ⟨https://zenodo.org/doi/10.5281/zenodo.13953629⟩.

BADGUJAR, C. M.; POULOSE, A.; GAN, H. Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review. Computers and Electronics in Agriculture, v. 223, p. 109090, 2024. ISSN 0168-1699. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S0168169924004812⟩.

BERGMANN, M. A. et al. An Approach Based on LiDAR and Spherical Images for Automated Vegetation Inspection in Urban Power Distribution Lines. IEEE Access, v. 12, p. 105119–105130, 2024.

VIJAYAKUMAR, A.; VAIRAVASUNDARAM, S. YOLO-based Object Detection Models: A Review and its Applications. Multimedia Tools and Applications, v. 83, n. 35, p. 83535–83574, Oct. 2024. ISSN 1573-7721. Disponível em: ⟨https://doi.org/10.1007/s11042-024-18872-y⟩.

AL., G. J. et al. YOLOv5 by Ultralytics. 2020. Disponível em: ⟨https://github.com/ultralytics/yolov5⟩.

AL., G. J. et al. Ultralytics YOLO. 2023. Original-date: 2022-09-11T16:39:45Z. Disponível em: ⟨https://github.com/ultralytics/ultralytics⟩.

SIRISHA, U. et al. Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection. International Journal of Computational Intelligence Systems, v. 16, n. 1, p. 126, Aug. 2023. ISSN 1875-6883. Disponível em: ⟨https://doi.org/10.1007/s44196-023-00302-w⟩.

WANG, C.-Y.; YEH, I.-H.; LIAO, H.-Y. M. YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. 2024. Disponível em: ⟨https://arxiv.org/abs/2402.13616⟩.

WANG, A. et al. YOLOv10: Real-Time End-to-End Object Detection. 2024. Disponível em: ⟨https://arxiv.org/abs/2405.14458⟩.

AHARON, S. et al. Deci-AI/super-gradients: 3.0.8. Zenodo, 2023. Disponível em: ⟨https://zenodo.org/records/7789328⟩.

DOSOVITSKIY, A. et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. 2021. Disponível em: ⟨https://arxiv.org/abs/2010.11929⟩.

CARION, N. et al. End-to-end object detection with transformers. In: VEDALDI, A. et al. (Ed.). Computer Vision – ECCV 2020. Cham: Springer International Publishing, 2020. p. 213–229.

DUDA, R. O.; HART, P. E.; STORK, D. G. Pattern Classification (2nd Edition). USA: Wiley-Interscience, 2000. ISBN 0471056693.