ArticleScientific reports2025
Predicting wheat yield using deep learning and multi-source environmental data.
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 6 papers.
What it found
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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.
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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.
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Who cites it
6 citing papers in PubMed.
- A feature-centric predictive and sensitivity analysis framework for bio-oil yield assessment in biomass pyrolysis.Scientific reports · 2026Article
- Wheat yield prediction using integrated optical and radar remote sensing with machine learning across key phenological stages.Scientific reports · 2026Article
- Metagenomic Tools as Predictive Approaches to Decipher Soil and Rhizosphere Microbial Communities and Their Functions for Sustainable Agriculture.International journal of genomics · 2026Review
- Transformer model to determine spatio-temporal relationships of variables, and interpretability for soybean seed yield, oil, and protein prediction.Frontiers in artificial intelligence · 2026Article
- Genetic enhancement of root, tuber and cereal crops via pangenomics, multi-omics integration and AI-driven prediction.Frontiers in plant science · 2026Review
- Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.Frontiers in plant science · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate forecasting of crop yields is essential for ensuring food security and promoting sustainable agricultural practices. Winter wheat, a key staple crop in Pakistan, faces challenges in yield prediction because of the complex interactions among climatic, soil, and environmental factors. This study introduces DeepAgroNet, a novel three-branch deep learning framework that integrates satellite imagery, meteorological data, and soil characteristics to estimate winter wheat yields at the district level in southern Pakistan. The framework employs three leading deep learning models-convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN)-trained on detrended yield data from 2017 to 2022. The Google Earth Engine platform was used to process and integrate remote sensing, climate, and soil data. CNN emerged as the most effective model, achieving an R
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Registered trials
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