ArticleFrontiers in plant science2022
Detecting strawberry diseases and pest infections in the very early stage with an ensemble deep-learning model.
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 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multimodal cross-attention network for overgrowth detection in strawberry seedlings.Frontiers in plant science · 2025Article
- Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation-a review.Frontiers in plant science · 2024Review
- Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles.Frontiers in plant science · 2023Article
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3 authors.
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Abstract
Detecting early signs of plant diseases and pests is important to preclude their progress and minimize the damages caused by them. Many methods are developed to catch signs of diseases and pests from plant images with deep learning techniques, however, detecting early signs is still challenging because of the lack of datasets to train subtle changes in plants. To solve these challenges, we built an automatic data acquisition system for the accumulation of a large dataset of plant images and trained an ensemble model to detect targeted plant diseases and pests. After obtaining 13,393 plant image data, our ensemble model shows a decent detection performance with an average of AUPRC 0.81. Also, this data acquisition and the detection process can be applied to other plant anomalies with the collection of additional data.
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