ReviewNeural computing & applications2022
Deep learning techniques to classify agricultural crops through UAV imagery: a review.
Review in Neural computing & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing large vision and language models in agriculture: a review.Frontiers in plant science · 2025Pooled it
- AgroPatch AutoStrike for adversarial patch attacks on plant disease detection models.iScience · 2026Article
- Adaptive graph contrastive learning with hard negative mining for multimodal hyperspectral and LiDAR classification.iScience · 2026Article
- Improved leaf area index estimation in Jujube trees by fusing spatial-temporal features from UAV RGB time-series with a deep learning framework.Frontiers in plant science · 2026Article
- AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas.Scientific reports · 2025Article
- Advancing Crop Resilience Through High-Throughput Phenotyping for Crop Improvement in the Face of Climate Change.Plants (Basel, Switzerland) · 2025Review
- Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learning.Scientific reports · 2025Article
- Enhanced object detection in remote sensing images by applying metaheuristic and hybrid metaheuristic optimizers to YOLOv7 and YOLOv8.Scientific reports · 2025Article
- Fine scale mapping of water sources in low-income settings: A comparative study in Misungwi, Tanzania.PloS one · 2025Article
- Application of UAV remote sensing for vegetation identification: a review and meta-analysis.Frontiers in plant science · 2025Review
- A bibliometric review of deep learning in crop monitoring: trends, challenges, and future perspectives.Frontiers in artificial intelligence · 2025Review
- Article
- Review
- Harnessing UAVs and deep learning for accurate grass weed detection in wheat fields: a study on biomass and yield implications.Plant methods · 2024Article
- An Improved Instance Segmentation Method for Complex Elements of Farm UAV Aerial Survey Images.Sensors (Basel, Switzerland) · 2024Article
- Enhancing two-stage object detection models via data-driven anchor box optimization in UAV-based maritime SAR.Scientific reports · 2024Article
- Article
- Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis.PeerJ. Computer science · 2024Article
- Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification.Biomimetics (Basel, Switzerland) · 2023Article
- Maize Production under Drought Stress: Nutrient Supply, Yield Prediction.Plants (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used to improve agriculture productivity while reducing drudgery, inspection time, and crop management cost. Moreover, they are able to cover large areas in a matter of a few minutes. Due to the impressive technological advancement, UAV-based remote sensing technologies are increasingly used to collect valuable data that could be used to achieve many precision agriculture applications, including crop/plant classification. In order to process these data accurately, we need powerful tools and algorithms such as Deep Learning approaches. Recently, Convolutional Neural Network (CNN) has emerged as a powerful tool for image processing tasks achieving remarkable results making it the state-of-the-art technique for vision applications. In the present study, we reviewed the recent CNN-based methods applied to the UAV-based remote sensing image analysis for crop/plant classification to help researchers and farmers to decide what algorithms they should use accordingly to their studied crops and the used hardware. Fusing different UAV-based data and deep learning approaches have emerged as a powerful tool to classify different crop types accurately. The readers of the present review could acquire the most challenging issues facing researchers to classify different crop types from UAV imagery and their potential solutions to improve the performance of deep learning-based algorithms.
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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.