ReviewFrontiers in plant science2024
Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection.
Review in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- UAV-based multispectral imaging and machine learning for detecting and mapping maize leaf diseases in smallholder farms.Scientific reports · 2026Article
- RustMAE: A Spatiotemporal Transformer for Short- to Medium-Term Warning of Wheat Stripe Rust Spring Spread.iScience · 2026Article
- Towards Robust UAV Navigation in Agriculture: Key Technologies, Application, and Future Directions.Plants (Basel, Switzerland) · 2026Review
- Smart farming approaches in medicinal plant cultivation: a review of techniques, benefits, and sustainability.Planta · 2026Review
- Challenges and strategies for harnessing large language models in plant protection.Frontiers in plant science · 2026Article
- AWAVM-UNet: adaptive-weighted attention VM-UNet with multiscale attention feature aggregation for grape disease detection from UAV imagery.Frontiers in plant science · 2026Article
- Multi-source UAV remote sensing for cotton Verticillium wilt resistance grading using TPAC-Net.Frontiers in plant science · 2026Article
- LINA's testing infrastructure enables AI to take-off in unmanned aerial vehicles (UAVs).Frontiers in robotics and AI · 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
- Fusion of Robotics, AI, and Thermal Imaging Technologies for Intelligent Precision Agriculture Systems.Sensors (Basel, Switzerland) · 2025Review
- Advances in UAV Remote Sensing for Monitoring Crop Water and Nutrient Status: Modeling Methods, Influencing Factors, and Challenges.Plants (Basel, Switzerland) · 2025Review
- YOLOV8-CMS: a high-accuracy deep learning model for automated citrus leaf disease classification and grading.Plant methods · 2025Article
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- Integrative Approaches to Soybean Resilience, Productivity, and Utility: A Review of Genomics, Computational Modeling, and Economic Viability.Plants (Basel, Switzerland) · 2025Review
- Spectroscopic detection of cotton Verticillium wilt by spectral feature selection and machine learning methods.Frontiers in plant science · 2025Article
- Deep learning-based anomaly detection for precision field crop protection.Frontiers in plant science · 2025Article
- Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Frontiers in plant science · 2025Article
- SRC-YOLOv8n: a lightweight framework for fine-grained apple leaf disease detection with spatial detail preservation and multi-scale feature enhancement.Frontiers in plant science · 2025Article
- Multiscale CNN-state space model with feature fusion for crop disease detection from UAV imagery.Frontiers in plant science · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future.
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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.