ArticleFrontiers in plant science2020
Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- Zinc and its binding proteins: essential roles and therapeutic potential.Archives of toxicology · 2025Review
- Insights into the functional role of Actinomycetia in promoting plant growth and biocontrol in tea (Camellia sinensis) plants.Archives of microbiology · 2024Review
- PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis.Plant phenomics (Washington, D.C.) · 2023Article
- Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning.Frontiers in plant science · 2023Article
- Automated Detection of Rice Bakanae Disease via Drone Imagery.Sensors (Basel, Switzerland) · 2022Article
- Transfer learning for versatile plant disease recognition with limited data.Frontiers in plant science · 2022Article
- Deep Metric Learning-Based Strawberry Disease Detection With Unknowns.Frontiers in plant science · 2022Article
- MDAM-DRNet: Dual Channel Residual Network With Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection.Frontiers in plant science · 2022Article
- Detection of unknown strawberry diseases based on OpenMatch and two-head network for continual learning.Frontiers in plant science · 2022Article
- Detecting strawberry diseases and pest infections in the very early stage with an ensemble deep-learning model.Frontiers in plant science · 2022Article
- An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN.Sensors (Basel, Switzerland) · 2021Article
- Open Set Self and Across Domain Adaptation for Tomato Disease Recognition With Deep Learning Techniques.Frontiers in plant science · 2021Article
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4 authors.
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Abstract
Detecting plant diseases in the earliest stages, when remedial intervention is most effective, is critical if damage crop quality and farm productivity is to be contained. In this paper, we propose an improved vision-based method of detecting strawberry diseases using a deep neural network (DNN) capable of being incorporated into an automated robot system. In the proposed approach, a backbone feature extractor named PlantNet, pre-trained on the PlantCLEF plant dataset from the LifeCLEF 2017 challenge, is installed in a two-stage cascade disease detection model. PlantNet captures plant domain knowledge so well that it outperforms a pre-trained backbone using an ImageNet-type public dataset by at least 3.2% in mean Average Precision (mAP). The cascade detector also improves accuracy by up to 5.25% mAP. The results indicate that PlantNet is one way to overcome the lack-of-annotated-data problem by applying plant domain knowledge, and that the human-like cascade detection strategy effectively improves the accuracy of automated disease detection methods when applied to strawberry plants.
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