ArticleFrontiers in plant science2019
Maize Canopy Temperature Extracted From UAV Thermal and RGB Imagery and Its Application in Water Stress Monitoring.
Article in Frontiers in plant science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- Multiparametric Regressions and Machine Learning: UAV Imagery for Temperature Prediction of Irrigated Forage Cactus in the Brazilian Semiarid.Sensors (Basel, Switzerland) · 2026Article
- Design of a Distance-Compensated Infrared Thermometer for Canopy Temperature Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- Multivariate and imaging methods to classify cold tolerance of sugarcane (Frontiers in plant science · 2026Article
- Estimating maize canopy water content using UAV-based multispectral-thermal infrared imagery and canopy signal distributional features.Frontiers in plant science · 2026Article
- DELTA-SoyStage: A Lightweight Detection Architecture for Full-Cycle Soybean Growth Stage Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field.Journal of experimental botany · 2025Article
- Multi-Scale Remote-Sensing Phenomics Integrated with Multi-Omics: Advances in Crop Drought-Heat Stress Tolerance Mechanisms and Perspectives for Climate-Smart Agriculture.Plants (Basel, Switzerland) · 2025Review
- Development of an automated phenotyping platform and identification of a novel QTL for drought tolerance in soybean.Plant phenomics (Washington, D.C.) · 2025Article
- The Evaluation of Small-Scale Field Maize Transpiration Rate from UAV Thermal Infrared Images Using Improved Three-Temperature Model.Plants (Basel, Switzerland) · 2025Article
- Integrating Genetic Diversity and Agronomic Innovations for Climate-Resilient Maize Systems.Plants (Basel, Switzerland) · 2025Review
- Semantic segmentation model of multi-source remote sensing images was used to extract winter wheat at tillering stage.Scientific reports · 2025Article
- Needle angle dynamics as a rapid indicator of drought stress inFrontiers in plant science · 2025Article
- Recent Methods for Evaluating Crop Water Stress Using AI Techniques: A Review.Sensors (Basel, Switzerland) · 2024Review
- Enhancing Winter Wheat Soil-Plant Analysis Development Value Prediction through Evaluating Unmanned Aerial Vehicle Flight Altitudes, Predictor Variable Combinations, and Machine Learning Algorithms.Plants (Basel, Switzerland) · 2024Article
- RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review.Plants (Basel, Switzerland) · 2024Review
- Practical Considerations and Limitations of Using Leaf and Canopy Temperature Measurements as a Stomatal Conductance Proxy: Sensitivity across Environmental Conditions, Scale, and Sample Size.Plant phenomics (Washington, D.C.) · 2024Article
- Review
- Use of Thermography to Evaluate Alternative Crops for Off-Season in the Cerrado Region.Plants (Basel, Switzerland) · 2023Article
- UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To identify drought-tolerant crop cultivars or achieve a balance between water use and yield, accurate measurements of crop water stress are needed. In this study, the canopy temperature (Tc) of maize at the late vegetative stage was extracted from high-resolution red-green-blue (RGB, 1.25 cm) and thermal (7.8 cm) images taken by an unmanned aerial vehicle (UAV). To reduce the number of parameters for crop water stress monitoring, four simple methods that require only Tc were identified: Tc, degrees above non-stress, standard deviation of Tc, and variation coefficient of Tc. The ground-truth temperatures obtained using a handheld infrared thermometer were used to calibrate the temperature obtained from the UAV thermal images and to evaluate the Tc extraction results. Measured leaf stomatal conductance values were used to evaluate the performance of the four Tc-based crop water stress indicators. The results showed a strong correlation between ground-truth Tc and Tc extracted by the red-green ratio index (RGRI)-Otsu method proposed in this study, with a coefficient of determination of 0.94 (
Indexed as
Identifiers
What OpenQuestion holds
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.