ReviewSensors (Basel, Switzerland)2020
Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Research progress on precision orchard yield estimation based on multi-source information perception sensors.Frontiers in plant science · 2026Pooled it
- Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects.Sensors (Basel, Switzerland) · 2022Pooled it
- AI-based object detection latest trends in remote sensing, multimedia and agriculture applications.Frontiers in plant science · 2022Pooled it
- YOLOv12n-RMB: An Improved Dual Target Recognition Network for Seedling Maize and Field Weeds in Cold Regions.Plants (Basel, Switzerland) · 2026Article
- Deep Learning-Based Monitoring of European Hamster Activity.Ecology and evolution · 2026Article
- AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas.Scientific reports · 2025Article
- WPDSI: A deep learning method for wheat phenology detection from single-temporal images.Plant phenomics (Washington, D.C.) · 2025Article
- Investigating vulnerabilities of gait recognition model using latent-based perturbations.Scientific reports · 2025Article
- Integrating UAV-Derived Diameter Estimations and Machine Learning for Precision Cabbage Yield Mapping.Sensors (Basel, Switzerland) · 2025Article
- Enhancing Instance Segmentation in Agriculture: An Optimized YOLOv8 Solution.Sensors (Basel, Switzerland) · 2025Article
- BED-YOLO: An Enhanced YOLOv10n-Based Tomato Leaf Disease Detection Algorithm.Sensors (Basel, Switzerland) · 2025Article
- Hybrid feature optimized CNN for rice crop disease prediction.Scientific reports · 2025Article
- Review
- Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Article
- Stereo Vision for Plant Detection in Dense Scenes.Sensors (Basel, Switzerland) · 2024Article
- Improved YOLO-FastestV2 wheat spike detection model based on a multi-stage attention mechanism with a LightFPN detection head.Frontiers in plant science · 2024Article
- Enhancing Green Fraction Estimation in Rice and Wheat Crops: A Self-Supervised Deep Learning Semantic Segmentation Approach.Plant phenomics (Washington, D.C.) · 2023Article
- Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review.Frontiers in plant science · 2023Review
- Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income.Plant phenomics (Washington, D.C.) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.
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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.