ArticleInternational journal of biometeorology2020
Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases.
Article in International journal of biometeorology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 87 citations in OpenAlex.
- Analysis of crop disease and pest occurrences: Insights from Japan's national surveys.PloS one · 2025Article
- Robust coffee plant disease classification using deep learning and advanced feature engineering techniques.PeerJ. Computer science · 2025Article
- Characteristic features of statistical models and machine learning methods derived from pest and disease monitoring datasets.Royal Society open science · 2023Article
- RustOnt: An Ontology to Explain Weather Favorable Conditions of the Coffee Rust.Sensors (Basel, Switzerland) · 2022Article
- Forecasting the seasonal dynamics of Trichoplusia ni (Lep.: Noctuidae) on three Brassica crops through neural networks.International journal of biometeorology · 2022Article
- Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data.Computational intelligence and neuroscience · 2022Article
- Prediction of Visual Acuity after anti-VEGF Therapy in Diabetic Macular Edema by Machine Learning.Journal of diabetes research · 2022Article
- Models for simulating the frequency of pests and diseases of Coffea arabica L.International journal of biometeorology · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disease and pest alert models are able to generate information for agrochemical applications only when needed, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models to be used in disease and pest warning systems as a function of the weather in order to improve the efficiency of chemical control of pests of the coffee tree. Thus, we correlated the infection rates with the weather variables and also calibrated and tested machine learning algorithms to predict the incidence of coffee rust, cercospora, coffee miner, and coffee borer. We used weather and field data obtained from coffee plantations in production in the southern regions of the State of Minas Gerais (SO
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Registered trials
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