ArticlePlant phenomics (Washington, D.C.)2019
High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass.
Article in Plant phenomics (Washington, D.C.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Transfer learning models for wheat ear detection on multi-source dataset.Scientific reports · 2025Article
- Improved Multi-Stage Rice Above-Ground Biomass Estimation Using Wavelet-Texture-Fused Vegetation Indices from UAV Remote Sensing.Plants (Basel, Switzerland) · 2025Article
- Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location.Plant methods · 2024Article
- Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images.Plant phenomics (Washington, D.C.) · 2023Article
- Image-Based Phenotyping for Non-Destructive In Situ Rice (Sensors (Basel, Switzerland) · 2022Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection.Biology · 2022Review
- Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification.Plant phenomics (Washington, D.C.) · 2022Article
- Seedling maize counting method in complex backgrounds based on YOLOV5 and Kalman filter tracking algorithm.Frontiers in plant science · 2022Article
- Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits.Plant phenomics (Washington, D.C.) · 2022Article
- End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses.Plant phenomics (Washington, D.C.) · 2022Article
- Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging.Plant methods · 2021Article
- Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution.Plant phenomics (Washington, D.C.) · 2021Article
- A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images.Plant phenomics (Washington, D.C.) · 2021Article
- The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines.Plant phenomics (Washington, D.C.) · 2021Article
- Field Phenomics: Will It Enable Crop Improvement?Plant phenomics (Washington, D.C.) · 2021Review
- Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.Communications biology · 2020Article
- Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review.Plant phenomics (Washington, D.C.) · 2020Review
- Repeated Multiview Imaging for Estimating Seedling Tiller Counts of Wheat Genotypes Using Drones.Plant phenomics (Washington, D.C.) · 2020Article
- Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods.Plant phenomics (Washington, D.C.) · 2020Article
Corrections and comments
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6 authors.
Funding
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Abstract
Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes. Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments. A high spatial resolution RGB camera was used to capture the residual stems standing straight after the cutting by the combine machine during harvest. It provided a ground spatial resolution better than 0.2 mm. A Faster Regional Convolutional Neural Network (Faster-RCNN) deep-learning model was first trained to identify the stems cross section. Results showed that the identification provided precision and recall close to 95%. Further, the balance between precision and recall allowed getting accurate estimates of the stem density with a relative RMSE close to 7% and robustness across the two experimental sites. The estimated stem density was also compared with the ear density measured in the field with traditional methods. A very high correlation was found with almost no bias, indicating that the stem density could be a good proxy of the ear density. The heritability/repeatability evaluated over 16 genotypes in one of the two experiments was slightly higher (80%) than that of the ear density (78%). The diameter of each stem was computed from the profile of gray values in the extracts of the stem cross section. Results show that the stem diameters follow a gamma distribution over each microplot with an average diameter close to 2.0 mm. Finally, the biovolume computed as the product of the average stem diameter, the stem density, and plant height is closely related to the above-ground biomass at harvest with a relative RMSE of 6%. Possible limitations of the findings and future applications are finally discussed.
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Registered trials
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