ReviewFrontiers in plant science2025
Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.
Review in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases.Plants (Basel, Switzerland) · 2026Review
- Recent physiological, genetic, and management perspectives on lodging resistance in rice.Planta · 2026Review
- WaveUNet+: Preserving Root System Architecture Integrity in In Situ Root Segmentation via a Unified Spectral-Spatial Framework.Plants (Basel, Switzerland) · 2026Article
- Rose leaf disease classification and severity estimation using an interpretable vision transformer-based multi-task framework.BMC plant biology · 2026Article
- Unlocking Grass Stress Resistance: Fungal Endophyte-Mediated Pathogen Recognition and RNA Regulation.International journal of molecular sciences · 2026Review
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- Mulberry Drought Diagnosis: Integrating Proximal Sensing and Metabolomics for Remote Monitoring.Plants (Basel, Switzerland) · 2026Article
- F2DMAS: a smartphone video-based 3D phenotyping workflow for potted plants in complex backgrounds.Frontiers in plant science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the development of artificial intelligence (AI) in complicated imaging and remote sensing technologies, plant research is transitioning from manual measurements to automated data collecting. High-throughput image-based phenotyping enables the precise and automated acquisition of traits across various spatial and temporal scales, ranging from controlled laboratory settings to intricate field. Furthermore, AI facilitates the combination of satellite observations, unmanned aerial vehicle (UAV) imaging, soil and climate data, and spatiotemporal information to enhance the precision of trait monitoring and yield prediction. These advances enhance the ability to evaluate and predict crop performance under variable environmental conditions. This paper offers a cross-disciplinary paradigm for accurate and sustainable modern agriculture by merging AI methodologies with plant phenotyping and yield forecasting.
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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.