ArticleSensors (Basel, Switzerland)2020
Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery.
Article 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 18 papers.
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Who cites it
18 citing papers in PubMed.
- Dynamic LoRA Fine-Tuning of DINOv3 for Multi-Component Pasture Biomass Estimation.Sensors (Basel, Switzerland) · 2026Article
- Aerial imagery and Segment Anything Model for architectural trait phenotyping to support genetic analysis in peanut breeding.Plant phenomics (Washington, D.C.) · 2025Article
- Image-based yield prediction for tall fescue using random forests and convolutional neural networks.Frontiers in plant science · 2025Article
- Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Frontiers in plant science · 2025Article
- Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Article
- Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model.Sensors (Basel, Switzerland) · 2024Article
- Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images.Plant phenomics (Washington, D.C.) · 2023Article
- Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview.Animal reproduction · 2023Article
- Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing.Frontiers in plant science · 2023Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- Deep Learning Regression Approaches Applied to Estimate Tillering in Tropical Forages Using Mobile Phone Images.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning for Strawberry Canopy Delineation and Biomass Prediction from High-Resolution Images.Plant phenomics (Washington, D.C.) · 2022Article
- Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops.Sensors (Basel, Switzerland) · 2021Article
- Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing.Sensors (Basel, Switzerland) · 2021Article
- A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field.Sensors (Basel, Switzerland) · 2021Article
- Genomic Selection in Tropical Forage Grasses: Current Status and Future Applications.Frontiers in plant science · 2021Review
- Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species
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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.