The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Plant Disease Detection Using Advanced Deep Learning Algorithms: A Case Study of Papaya Ring Spot Disease
Abstract
Faster and accurate detection of plant diseases can enhance agricultural productivity and overall yield. Many methods are proposed in the literature for plant disease detection using optical sensors mounted on agricultural robots. There are many deep learning algorithms proposed to identify plant diseases. Many such algorithms require high computation power and take more time during training. Implementation of such algorithms on robot controllers for online detection is quite difficult. In this paper, we propose the use of lighter versions of YOLO which are more efficient and have high detection speed in plant disease detection. A case study of deadly viral disease that affects Papaya plants is considered, and a dataset consisting of images of both healthy and diseased leaves which are affected by the Papaya ringspot virus has been constructed. The proposed algorithms have been trained on the dataset built and tested. Test results show that lighter versions of YOLO are far more efficient than improved or original versions of YOLO, especially in the case of plant disease detection using mobile agricultural robots.
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“91.6% MAP”
From YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO (2024) · cites this paper as [61]
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Cites
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Research works printed but not linked: 3
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- 0, YOLO-Fastest
Cited by
YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO
Hussain, M. (2024) · IEEE Access · doi:10.1109/access.2024.3378568 · Cited by 492
Plant leaf disease detection and classification using convolution neural networks model: a review
Salka, T. D., Hanafi, M. B., Rahman, S. M. S. A. A., Zulperi, D. M., & Omar, Z. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11234-6 · Cited by 90
One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
Li, Y., Sun, S., Zhang, C., Yang, G., & Ye, Q. (2022) · Applied Sciences · doi:10.3390/app12167960 · Cited by 58
Real-time Joint Angle Estimation using Mediapipe Framework and Inertial Sensors
Palani, P., Panigrahi, S., Jammi, S. A., & Thondiyath, A. (2022) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe55377.2022.00035 · Cited by 32
Self-citationCrops Disease Detection, from Leaves to Field: What We Can Expect from Artificial Intelligence
Lebrini, Y., & Gotor, A. A. (2024) · Agronomy · doi:10.3390/agronomy14112719 · Cited by 28
Metadata
- Authors
- Maski, P., & Thondiyath, A.
- Affiliations
- Indian Institute of Technology Madras (IN)
- ORCID
- Thondiyath, Asokan
- Venue
- 2021 6th International Conference on Image, Vision and Computing (ICIVC)
- Publisher
- IEEE
- Published
- 2021-07-23
- Pages
- 49-54
- OpenAlex
- W3200159991
- Cited by
- 34
- References
- 21
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 14 · 2023: 8 · 2022: 4
- FWCI
- 10.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Image, Vision and Computing (ICIVC) (publishedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- plant disease detection, papaya ringspot virus, papaya, YOLO, lightweight object detection, leaf image dataset, plant virus disease
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- Maski, P., & Thondiyath, A. (2021). Plant Disease Detection Using Advanced Deep Learning Algorithms: A Case Study of Papaya Ring Spot Disease. 2021 6th International Conference on Image, Vision and Computing (ICIVC). https://doi.org/10.1109/icivc52351.2021.9526944
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