ArticleFrontiers in plant science2021
Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass.Scientific reports · 2024Article
- Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning.Frontiers in plant science · 2024Article
- Deep Learning Regression Approaches Applied to Estimate Tillering in Tropical Forages Using Mobile Phone Images.Sensors (Basel, Switzerland) · 2022Article
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7 authors.
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Abstract
Evaluation of the legume proportion in grass-legume mixed swards is necessary for breeding and for cultivation research of forage. For objective and time-efficient estimation of legume proportion, convolutional neural network (CNN) models were trained by fine-tuning the GoogLeNet to estimate the coverage of timothy (TY), white clover (WC), and background (Bg) on the unmanned aerial vehicle-based images. The accuracies of the CNN models trained on different datasets were compared using the mean bias error and the mean average error. The models predicted the coverage with small errors when the plots in the training datasets were similar to the target plots in terms of coverage rate. The models that are trained on datasets of multiple plots had smaller errors than those trained on datasets of a single plot. The CNN models estimated the WC coverage more precisely than they did to the TY and the Bg coverages. The correlation coefficients (
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