ArticlePlants (Basel, Switzerland)2022
YOLO-JD: A Deep Learning Network for Jute Diseases and Pests Detection from Images.
Article in Plants (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 90 citations in OpenAlex.
- A YOLO11-MSC-Based Method for Small Pest Detection in Insect Pest Monitoring Lamp Images.Sensors (Basel, Switzerland) · 2026Article
- A novel approach for disease and pests detection in potato production system based on deep learning.Scientific reports · 2026Article
- Pepper-4D: Spatiotemporal 3D Pepper Crop Dataset for Phenotyping.Plants (Basel, Switzerland) · 2026Article
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- Robust multi-target multi-scale tomato leaf disease detection for precision agriculture applications.Frontiers in plant science · 2026Article
- PAD-Net: a lightweight end-to-end detector for pepper anthracnose in complex agricultural environments.Frontiers in plant science · 2026Article
- Focal-HAIN: a lightweight model with adaptive modulation and hierarchical interaction for real-time crop pest and disease monitoring.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
- Recent advances in plant disease detection: challenges and opportunities.Plant methods · 2025Review
- Mamba-YOLO-ML: A State-Space Model-Based Approach for Mulberry Leaf Disease Detection.Plants (Basel, Switzerland) · 2025Article
- Exploring the impact of hyperparameter and data augmentation in YOLO V10 for accurate bone fracture detection from X-ray images.Scientific reports · 2025Article
- 3D-NOD: 3D new organ detection in plant growth by a spatiotemporal point cloud deep segmentation framework.Plant phenomics (Washington, D.C.) · 2025Article
- A Transformer-Based Detection Network for Precision Cistanche Pest and Disease Management in Smart Agriculture.Plants (Basel, Switzerland) · 2025Article
- An improved YOLOv8n model for in-field detection of pests and diseases in pakchoi.Frontiers in plant science · 2025Article
- Article
- YOLOv8 forestry pest recognition based on improved re-parametric convolution.Frontiers in plant science · 2025Article
- AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data.Plants (Basel, Switzerland) · 2024Article
- Intelligent Detection Method for Wildlife Based on Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management.Journal of imaging · 2023Article
- High-Precision Tomato Disease Detection Using NanoSegmenter Based on Transformer and Lightweighting.Plants (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Recently, disease prevention in jute plants has become an urgent topic as a result of the growing demand for finer quality fiber. This research presents a deep learning network called YOLO-JD for detecting jute diseases from images. In the main architecture of YOLO-JD, we integrated three new modules such as Sand Clock Feature Extraction Module (SCFEM), Deep Sand Clock Feature Extraction Module (DSCFEM), and Spatial Pyramid Pooling Module (SPPM) to extract image features effectively. We also built a new large-scale image dataset for jute diseases and pests with ten classes. Compared with other state-of-the-art experiments, YOLO-JD has achieved the best detection accuracy, with an average mAP of 96.63%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.