ArticleFrontiers in plant science2022
Data-centric annotation analysis for plant disease detection: Strategy, consistency, and performance.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 31 citations in OpenAlex.
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- Weakly supervised learning through box annotations for pig instance segmentation.Scientific reports · 2025Article
- Comprehensive plant health monitoring: expert-level assessment with spatio-temporal image data.Frontiers in plant science · 2025Article
- Enhancing anomaly detection in plant disease recognition with knowledge ensemble.Frontiers in plant science · 2025Article
- The impact of fine-tuning paradigms on unknown plant diseases recognition.Scientific reports · 2024Article
- Review
- A method for small-sized wheat seedlings detection: from annotation mode to model construction.Plant methods · 2024Article
- Plant disease recognition datasets in the age of deep learning: challenges and opportunities.Frontiers in plant science · 2024Article
- Counting Canola: Toward Generalizable Aerial Plant Detection Models.Plant phenomics (Washington, D.C.) · 2024Article
- Pig Movement Estimation by Integrating Optical Flow with a Multi-Object Tracking Model.Sensors (Basel, Switzerland) · 2023Article
- An iterative noisy annotation correction model for robust plant disease detection.Frontiers in plant science · 2023Article
- A New Deep Learning-based Dynamic Paradigm Towards Open-World Plant Disease Detection.Frontiers in plant science · 2023Article
- Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning.Frontiers in plant science · 2023Article
- Local refinement mechanism for improved plant leaf segmentation in cluttered backgrounds.Frontiers in plant science · 2023Article
- Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests.Frontiers in plant science · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improved the performance by ameliorating networks and optimizing the loss function. However, because of the vast influence of data annotation quality and the cost of annotation, the data-centric part of a project also needs more investigation. We should further consider the relationship between data annotation strategies, annotation quality, and the model's performance. In this paper, a systematic strategy with four annotation strategies for plant disease detection is proposed: local, semi-global, global, and symptom-adaptive annotation. Labels with different annotation strategies will result in distinct models' performance, and their contrasts are remarkable. An interpretability study of the annotation strategy is conducted by using class activation maps. In addition, we define five types of inconsistencies in the annotation process and investigate the severity of the impact of inconsistent labels on model's performance. Finally, we discuss the problem of label inconsistency during data augmentation. Overall, this data-centric quantitative analysis helps us to understand the significance of annotation strategies, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection. Our work encourages researchers to pay more attention to annotation consistency and the essential issues of annotation strategy. The code will be released at: https://github.com/JiuqingDong/PlantDiseaseDetection_Yolov5 .
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