ArticleCluster computing2023
A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images.
Article in Cluster computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Intelligent agricultural machinery navigation and pose estimation in plantation/nursery environments integrating improved RANSAC and lightweight YOLOv11.Scientific reports · 2026Article
- CADP: Connection-Aware DenseNet Pruning for lightweight plant disease classification.BMC plant biology · 2026Article
- Artificial Intelligence-Driven Plant Disease Detection and Diagnosis: A Comprehensive Review of Deep Learning Approaches, Multimodal Sensing Technologies, and Future Perspectives in Precision Agriculture.The plant pathology journal · 2026Review
- AWAVM-UNet: adaptive-weighted attention VM-UNet with multiscale attention feature aggregation for grape disease detection from UAV imagery.Frontiers in plant science · 2026Article
- CFAEVM-UNet: cross-fusion attention enhanced VM-UNet for UAV image segmentation of small and dense field crop pests.Frontiers in plant science · 2026Article
- ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images.Sensors (Basel, Switzerland) · 2025Article
- A Feature-Enhanced Small Object Detection Algorithm Based on Attention Mechanism.Sensors (Basel, Switzerland) · 2025Article
- Multiscale CNN-state space model with feature fusion for crop disease detection from UAV imagery.Frontiers in plant science · 2025Article
- OnionFoliageSET: Labeled dataset for small onion and foliage flower crop detection.Data in brief · 2024Article
- Article
- Harnessing the power of machine learning for crop improvement and sustainable production.Frontiers in plant science · 2024Review
- Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections.Frontiers in plant science · 2024Article
- Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis.PeerJ. Computer science · 2024Article
- UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios.Sensors (Basel, Switzerland) · 2023Article
- Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region.Sensors (Basel, Switzerland) · 2023Article
- Identification of plant leaf diseases by deep learning based on channel attention and channel pruning.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers' naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.
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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.