ArticlePlant phenomics (Washington, D.C.)2023
Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Review
- High-Throughput Phenotyping: Status and Applications in Rice Breeding.Plants (Basel, Switzerland) · 2026Review
- A parallel resnet-transformer and cross-modal attention network for identification of rice blast resistance using UAV-based visible-infrared imagery.Plant phenomics (Washington, D.C.) · 2026Article
- From triangle to pyramid: Understanding host-pathogen-microniome-environment interplay for sustainable, enviromics-empowered management of plant diseases.Plant communications · 2026Review
- Integrating image-based phenotyping and QTL mapping to enhance genetic resistance and accelerate breeding for bacterial grain rot resistance in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.Plant communications · 2026Review
- XooNet: a high-throughput UAV-based approach for field screening of bacterial blight-resistant germplasm in wild rice.Frontiers in plant science · 2026Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Winter wheat yield prediction using UAV-based multivariate time series data and variate-independent tokenization.Plant phenomics (Washington, D.C.) · 2025Article
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Combining UAV multisensor field phenotyping and genome-wide association studies to reveal the genetic basis of plant height in cotton (Plant phenomics (Washington, D.C.) · 2025Article
- Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Frontiers in plant science · 2025Article
- A novel method for detecting brown planthopper (Frontiers in plant science · 2025Article
- Rice breeding for low input agriculture.Frontiers in plant science · 2024Review
- Fantastic genes: where and how to find them? Exploiting rice genetic resources for the improvement of yield, tolerance, and resistance to a wide array of stresses in rice.Functional & integrative genomics · 2023Review
- Development of a Quick-Install Rapid Phenotyping System.Sensors (Basel, Switzerland) · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bacterial blight poses a threat to rice production and food security, which can be controlled through large-scale breeding efforts toward resistant cultivars. Unmanned aerial vehicle (UAV) remote sensing provides an alternative means for the infield phenotype evaluation of crop disease resistance to relatively time-consuming and laborious traditional methods. However, the quality of data acquired by UAV can be affected by several factors such as weather, crop growth period, and geographical location, which can limit their utility for the detection of crop disease and resistant phenotypes. Therefore, a more effective use of UAV data for crop disease phenotype analysis is required. In this paper, we used time series UAV remote sensing data together with accumulated temperature data to train the rice bacterial blight severity evaluation model. The best results obtained with the predictive model showed an
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.