ArticleScientific reports2025
Enhanced wheat yield prediction through integrated climate and satellite data using advanced AI techniques.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Design and evaluation of bayesian optimized hybrid deep learning model for forecasting crop yields using climate dynamics.Scientific reports · 2026Article
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- Cereal and Cereal Products: Quality, Functionality, Health Security and Application of New Technologies.Foods (Basel, Switzerland) · 2026Article
- Predicting wheat yield using deep learning and multi-source environmental data.Scientific reports · 2025Article
- Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.Frontiers in plant science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wheat plays a vital role in Pakistan's economy and food security, making accurate yield forecasting essential for planning and resource management. Traditional approaches-such as manual field surveys and remote sensing-have been widely used, but their effectiveness in capturing yield variation across different growth stages remains uncertain. This study explores a multi-phase approach to wheat yield prediction by dividing the crop cycle into four key stages and analyzing data from 2017 to 2022. Using the Google Earth Engine platform, the analysis integrates satellite imagery, seasonal weather variables, and soil information. A range of machine learning and deep learning models were tested to assess their predictive performance. The results show that combining diverse data sources with advanced AI techniques significantly improves prediction accuracy, with model performance reaching R
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.