ArticlePlant physiology2021
Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms.
Article in Plant physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 149 citations in OpenAlex.
- Assessing cotton boll-opening concentration for harvest decision-making via foundation model-enhanced cross-scale phenotyping.Plant phenomics (Washington, D.C.) · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- A hybrid PROSAIL inversion framework for winter wheat LCC using hyperspectral data and transfer learning.Frontiers in plant science · 2026Article
- Improved leaf area index estimation in Jujube trees by fusing spatial-temporal features from UAV RGB time-series with a deep learning framework.Frontiers in plant science · 2026Article
- High-Throughput Field Phenotyping Using Unmanned Aerial Vehicles (UAVs) for Rapid Estimation of Photosynthetic Traits.Plant phenomics (Washington, D.C.) · 2025Article
- Harder, better, faster, stronger, and with less annotated data: ESGAN and plant sciences.Plant physiology · 2025Article
- Breaking the field phenotyping bottleneck in maize with autonomous robots.Communications biology · 2025Article
- 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
- Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale.BMC plant biology · 2025Article
- Plant Detection in RGB Images from Unmanned Aerial Vehicles Using Segmentation by Deep Learning and an Impact of Model Accuracy on Downstream Analysis.Journal of imaging · 2025Article
- Field-deployable lightweight YOLOv8n for real-time detection and counting of Maize seedlings using UAV RGB imagery.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
- Orchard variable-rate spraying method integrating GNSS and wind-excited audio-conducted leaf area density.Frontiers in plant science · 2025Article
- UAV-based multitier feature selection improves nitrogen content estimation in arid-region cotton.Frontiers in plant science · 2025Article
- Estimating the full-period rice leaf area index using CNN-LSTM-Attention and multispectral images from unmanned aerial vehicles.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
- Faba bean and pea harvest index estimations using aerial-based multimodal data and machine learning algorithms.Plant physiology · 2024Article
- Prediction of wheat SPAD using integrated multispectral and support vector machines.Frontiers in plant science · 2024Article
- Comparison of Different Machine Learning Algorithms for the Prediction of the Wheat Grain Filling Stage Using RGB Images.Plants (Basel, Switzerland) · 2023Article
- Soybean leaf estimation based on RGB images and machine learning methods.Plant methods · 2023Article
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Authors and funding
11 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Measuring leaf area index (LAI) is essential for evaluating crop growth and estimating yield, thereby facilitating high-throughput phenotyping of maize (Zea mays). LAI estimation models use multi-source data from unmanned aerial vehicles (UAVs), but using multimodal data to estimate maize LAI, and the effect of tassels and soil background, remain understudied. Our research aims to (1) determine how multimodal data contribute to LAI and propose a framework for estimating LAI based on remote-sensing data, (2) evaluate the robustness and adaptability of an LAI estimation model that uses multimodal data fusion and deep neural networks (DNNs) in single- and whole growth stages, and (3) explore how soil background and maize tasseling affect LAI estimation. To construct multimodal datasets, our UAV collected red-green-blue, multispectral, and thermal infrared images. We then developed partial least square regression (PLSR), support vector regression, and random forest regression models to estimate LAI. We also developed a deep learning model with three hidden layers. This multimodal data structure accurately estimated maize LAI. The DNN model provided the best estimate (coefficient of determination [R2] = 0.89, relative root mean square error [rRMSE] = 12.92%) for a single growth period, and the PLSR model provided the best estimate (R2 = 0.70, rRMSE = 12.78%) for a whole growth period. Tassels reduced the accuracy of LAI estimation, but the soil background provided additional image feature information, improving accuracy. These results indicate that multimodal data fusion using low-cost UAVs and DNNs can accurately and reliably estimate LAI for crops, which is valuable for high-throughput phenotyping and high-spatial precision farmland management.
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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.