ArticleFrontiers in plant science2022
An automatic identification system for citrus greening disease (Huanglongbing) using a YOLO convolutional neural network.
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 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
- High-fidelity crop modelling: innovations driving sustainable fruit horticulture.Frontiers in plant science · 2026Review
- An effective citrus ripeness detection model for complex orchard scenarios.Frontiers in plant science · 2026Article
- Deep vision in agriculture: assessing the function of YOLO in the classification of plant leaf diseases (PLDs).BioData mining · 2025Article
- Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head.Sensors (Basel, Switzerland) · 2025Article
- Supervised Hyperspectral Band Selection Using Texture Features for Classification of Citrus Leaf Diseases with YOLOv8.Sensors (Basel, Switzerland) · 2025Article
- Early detection of Citrus Huanglongbing by UAV remote sensing based on MGA-UNet.Frontiers in plant science · 2025Article
- YOLOv5s-Based Image Identification of Stripe Rust and Leaf Rust on Wheat at Different Growth Stages.Plants (Basel, Switzerland) · 2024Article
- Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning.Scientific reports · 2024Article
- Real-time and lightweight detection of grape diseases based on Fusion Transformer YOLO.Frontiers in plant science · 2024Article
- Article
- Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model.Plant phenomics (Washington, D.C.) · 2023Article
- 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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Abstract
Huanglongbing (HLB), or citrus greening disease, has complex and variable symptoms, making its diagnosis almost entirely reliant on subjective experience, which results in a low diagnosis efficiency. To overcome this problem, we constructed and validated a deep learning (DL)-based method for detecting citrus HLB using YOLOv5l from digital images. Three models (Yolov5l-HLB1, Yolov5l-HLB2, and Yolov5l-HLB3) were developed using images of healthy and symptomatic citrus leaves acquired under a range of imaging conditions. The micro F1-scores of the Yolov5l-HLB2 model (85.19%) recognising five HLB symptoms (blotchy mottling, "red-nose" fruits, zinc-deficiency, vein yellowing, and uniform yellowing) in the images were higher than those of the other two models. The generalisation performance of Yolov5l-HLB2 was tested using test set images acquired under two photographic conditions (conditions B and C) that were different from that of the model training set condition (condition A). The results suggested that this model performed well at recognising the five HLB symptom images acquired under both conditions B and C, and yielded a micro F1-score of 84.64% and 85.84%, respectively. In addition, the detection performance of the Yolov5l-HLB2 model was better for experienced users than for inexperienced users. The PCR-positive rate of
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