ArticlePlant phenomics (Washington, D.C.)2021
Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution.
Article in Plant phenomics (Washington, D.C.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Digitalised phenotyping of pepper (Capsicum spp.) using effective RGB imaging and optimised camera positioning.Scientific reports · 2026Article
- Dynamic maize true leaf area index retrieval with KGCNN and TL and integrated 3D radiative transfer modeling for crop phenotyping.Plant phenomics (Washington, D.C.) · 2025Article
- Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models.Scientific reports · 2025Article
- Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2024Article
- Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery.Plant phenomics (Washington, D.C.) · 2024Article
- YOLO SSPD: a small target cotton boll detection model during the boll-spitting period based on space-to-depth convolution.Frontiers in plant science · 2024Article
- Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Plant phenomics (Washington, D.C.) · 2024Article
- VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation.Scientific data · 2023Article
- Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys.Scientific reports · 2023Article
- Maize plant detection using UAV-based RGB imaging and YOLOv5.Frontiers in plant science · 2023Article
- A Multiscale Point-Supervised Network for Counting Maize Tassels in the Wild.Plant phenomics (Washington, D.C.) · 2023Article
- Analyzing Changes in Maize Leaves Orientation due to GxExM Using an Automatic Method from RGB Images.Plant phenomics (Washington, D.C.) · 2023Article
- Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income.Plant phenomics (Washington, D.C.) · 2023Article
- Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery.Frontiers in plant science · 2023Article
- A Novel Intelligent System for Dynamic Observation of Cotton Verticillium Wilt.Plant phenomics (Washington, D.C.) · 2023Article
- A Tea Buds Counting Method Based on YOLOv5 and Kalman Filter Tracking Algorithm.Plant phenomics (Washington, D.C.) · 2023Article
- From Prototype to Inference: A Pipeline to Apply Deep Learning in Sorghum Panicle Detection.Plant phenomics (Washington, D.C.) · 2023Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data.Frontiers in plant science · 2022Article
- Multispectral Drone Imagery and SRGAN for Rapid Phenotypic Mapping of Individual Chinese Cabbage Plants.Plant phenomics (Washington, D.C.) · 2022Article
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Authors and funding
7 authors.
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Abstract
Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput, accuracy, and access to plant localization. However, high-resolution images are required to detect the small plants present at the early stages. This study explores the impact of image ground sampling distance (GSD) on the performances of maize plant detection at three-to-five leaves stage using Faster-RCNN object detection algorithm. Data collected at high resolution (GSD ≈ 0.3 cm) over six contrasted sites were used for model training. Two additional sites with images acquired both at high and low (GSD ≈ 0.6 cm) resolutions were used to evaluate the model performances. Results show that Faster-RCNN achieved very good plant detection and counting (rRMSE = 0.08) performances when native high-resolution images are used both for training and validation. Similarly, good performances were observed (rRMSE = 0.11) when the model is trained over synthetic low-resolution images obtained by downsampling the native training high-resolution images and applied to the synthetic low-resolution validation images. Conversely, poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial resolution. Training on a mix of high- and low-resolution images allows to get very good performances on the native high-resolution (rRMSE = 0.06) and synthetic low-resolution (rRMSE = 0.10) images. However, very low performances are still observed over the native low-resolution images (rRMSE = 0.48), mainly due to the poor quality of the native low-resolution images. Finally, an advanced super resolution method based on GAN (generative adversarial network) that introduces additional textural information derived from the native high-resolution images was applied to the native low-resolution validation images. Results show some significant improvement (rRMSE = 0.22) compared to bicubic upsampling approach, while still far below the performances achieved over the native high-resolution images.
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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.