ArticleSensors (Basel, Switzerland)2024
Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data.
Article in Sensors (Basel, Switzerland), 2024. 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.
- Molecular Targets and Trait Innovation in CRISPR-Edited Vegetable Crops: An Up-to-Date Review (2020-2026).Plants (Basel, Switzerland) · 2026Review
- Green synthesis of demethoxycurcumin-loaded chitosan nanoparticles for the management of potato late blight caused by Phytophthora infestans.International journal of biological macromolecules · 2025Article
- CRISPR-Cas Gene Editing Technology in Potato.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
While past research has emphasized the importance of late blight infection detection and classification, anticipating the potato late blight infection is crucial from the economic point of view as it helps to significantly reduce the production cost. Furthermore, it is necessary to minimize the exposure of potatoes to harmful chemicals and pesticides due to their potential adverse effects on the human immune system. Our work is based on the precise classification of late blight infections in potatoes in European countries using real-time data from 1980 to 2000. To predict the potato late blight outbreak, we incorporated several hybrid machine learning models, as well as a unique combination of stacking classifier and logistic regression, achieving the highest prediction accuracy of 87.22%. Further enhancements of these models and the use of new data sources may lead to a higher late blight prediction accuracy and, consequently, a higher efficiency in managing potatoes' health.
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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.