ArticleScientific reports2025
Predicting crop disease severity using real time weather variability through machine learning algorithms.
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 3 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.
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.
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Who cites it
3 citing papers in PubMed.
- Early Warning of Cucumber Angular Leaf Spot by Estimating Airborne Pathogen Aerosols with Particulate Matter Sensors.Plants (Basel, Switzerland) · 2026Article
- Digital twins for plant-microbe interactions: Gap finding and filling.Plant communications · 2026Article
- Machine learning and time-series approaches for forecasting bacterial blight of pomegranate.Frontiers in plant science · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating disease severity with real-time meteorological variables and advanced machine learning techniques has provided valuable predictive insights for assessing disease severity in wheat. This study emphasizes the potential of machine learning models, particularly artificial neural networks (ANN), in predicting wheat disease severity with high accuracy. The field experiment was conducted over two consecutive rabi growing seasons (2023 And 2024) using a randomized block design with four sowing dates to investigate critical weather-disease relationships for two key wheat pathogens: Puccinia striiformis f. sp. tritici (yellow rust) and Blumeria graminis f. sp. tritici (powdery mildew). Weekly assessments of disease severity were combined with meteorological data and analyzed using ANN and regularized regression models. The ANN model demonstrated superior predictive accuracy for yellow rust and powdery mildew, achieving R-squared values (R
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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.