DMI and QoI fungicides for the control of coffee leaf rust

Honorato, Jaime; Zambolim, Laércio; Lopes, Uilton do Nascimento; Lopes, Ueder Pedro; Duarte, Henrique da Silva Silveira

doi:10.1007/s13313-015-0373-4

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…This is because the persistence of free water on the surface of plants plays an essential role in certain epidemiological processes, such as infection and sporulation (Huber and Gillespie 1992). Despite the development of disease-resistant cultivars, most farmers still grow cultivars susceptible to diseases (Honorato et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Wetness Duration Using Machine Learning Models

Silva,

Santos,

Rolim

et al. 2024

Preprint

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The leaf wetness duration (LWD) is one of the most critical parameters related to the infection rate and development of plant diseases, as many pathogens require the presence of free water on plant organs to infect leaf tissue. For this reason, this study evaluated three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network Multilayer Perceptron (MLP), using hourly surface meteorological inputs to estimate LWP. The models were trained and tested using 20 years of meteorological data for seven agricultural locations situated in the southern region of the state of Minas Gerais and the Triângulo Mineiro region. The models were compared with an empirical model based on observed relative humidity (NHUR>90%). The results indicated that machine learning models are capable of estimating LWD; however, among all methods tested, the MLP model demonstrated the best performance, with high accuracy (mean R² = 0.98) and low errors (mean RMSE = 27.6 minutes and mean MAE = 19.8 minutes). The results also showed that the models are sensitive to the study locations, with models for the southern Minas Gerais locations performing better than those for the Triângulo Mineiro region. This discrepancy is due to the lower LWD in the Triângulo Mineiro locations, which were underestimated by the models, resulting in lower performance.

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Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Wetness Duration Using Machine Learning Models

Silva,

Santos,

Rolim

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Under these conditions, producers cultivate varieties resistant to these climate conditions, that is, to drought and frost, during the winter or specific periods, and also varieties adaptable to new cultivation technologies, resulting in increased productivity [3]. Nevertheless, the high productivity associated with the favorable environment and the disease susceptibility of cultivars can lead to significant losses in the main Brazilian producing regions, principally in the Minas Gerais state [4][5][6][7][8][9][10][11][12]. In Brazil, loss of productivity associated with the disease can reach up to 30% [13].…”

Section: Introductionmentioning

confidence: 99%

Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil

et al. 2021

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This study aimed to develop a warning system platform for coffee rust incidence fifteen days in advance, as well as validating and regionalizing multiple linear regression models based on meteorological variables. The models developed by Pinto were validated in five counties. Experiments were set up in a randomized block design with five treatments and five replications. The experimental plot had six lines with 20 central plants of useful area. Assessments of coffee rust incidence were carried out fortnightly. The data collected from automatic stations were adjusted in new multiple linear regression models (MLRM) for five counties. Meteorological variables were lagged concerning disease assessment dates. After the adjustments, two models were selected and calculated for five counties, later there was an expansion to include ten more counties and 35 properties to validate these models. The result showed that the adjusted models of 15–30 days before rust incidence for Carmo do Rio Claro and Nova Resende counties were promising. These models were the best at forecasting disease 15 days in advance. With these models and the geoinformation systems, the warning platform and interface will be improved in the coffee grower region of the south and savannas of the Minas Gerais State, Brazil.

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