ArticlePlant methods2020
Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level.
Article in Plant methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- High-throughput screening of heat stress response in Chinese cabbage (Plant phenomics (Washington, D.C.) · 2026Article
- Instance-level phenotype-based growth stage classification of basil in multi-plant environments.Frontiers in plant science · 2025Article
- A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture.Sensors (Basel, Switzerland) · 2024Review
- 3D Morphological Feature Quantification and Analysis of Corn Leaves.Plant phenomics (Washington, D.C.) · 2024Article
- Nitrogen monitoring and inversion algorithms of fruit trees based on spectral remote sensing: a deep review.Frontiers in plant science · 2024Review
- SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation.Plant phenomics (Washington, D.C.) · 2024Article
- Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing.Plant methods · 2023Article
- Applications of LiDAR in Agriculture and Future Research Directions.Journal of imaging · 2023Review
- Enhancing crop yields through improvements in the efficiency of photosynthesis and respiration.The New phytologist · 2023Review
- The Importance of Using Realistic 3D Canopy Models to Calculate Light Interception in the Field.Plant phenomics (Washington, D.C.) · 2023Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images.Sensors (Basel, Switzerland) · 2022Article
- Non-destructive Plant Biomass Monitoring With High Spatio-Temporal ResolutionFrontiers in plant science · 2022Article
- Simultaneous Prediction of Wheat Yield and Grain Protein Content Using Multitask Deep Learning from Time-Series Proximal Sensing.Plant phenomics (Washington, D.C.) · 2022Article
- Article
- Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series.Plant phenomics (Washington, D.C.) · 2021Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrecision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms.
resultsIn this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R
conclusionWe believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices.
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