Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques

Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to preve...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-05, Vol.15 (9), p.2450
Main Authors: Shahi, Tej Bahadur, Xu, Cheng-Yuan, Neupane, Arjun, Guo, William
Format: Article
Language:English
Subjects:
Aerial photography
Agricultural production
Agricultural research
Artificial intelligence
crop disease
Crop diseases
Crop yield
Crop yields
Crops
Data analysis
Deep learning
detection
Disease detection
drone
Drone aircraft
Drone vehicles
Estimation
Image processing
Information management
Internet of Things
Learning algorithms
Machine learning
Plant diseases
Precision farming
Radiation
Remote sensing
Remote sensors
Sensors
Taxonomy
UAV
Unmanned aerial vehicles
Vegetation
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Summary:Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15092450