YOLO-JD: A Deep Learning Network for Jute Diseases and Pests Detection from Images

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 module...

Full description

Saved in:
Bibliographic Details
Published in:Plants (Basel) 2022-03, Vol.11 (7), p.937
Main Authors: Li, Dawei, Ahmed, Foysal, Wu, Nailong, Sethi, Arlin I
Format: Article
Language:English
Subjects:
Agricultural production
Agriculture
Artificial intelligence
Classification
Crop diseases
Datasets
Deep learning
disease detection
Disease prevention
Farmers
Feature extraction
image processing
Jute
Machine learning
Medical imaging
Modules
Pests
Plant diseases
Polymers
Rain
Sand
YOLO-JD
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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%.
ISSN:2223-7747
2223-7747
DOI:10.3390/plants11070937