Spray mixture volume in the control of Asian soybean rust

Nascimento, Roxana Stefane Mendes; Ferreira, Lino Roberto; Zambolim, Laércio; Parreira, D. F.; Costa, Yanna Karoline Santos da; Damascena, Jossimara Ferreira; Cecon, Paulo R.

doi:10.1016/j.cropro.2021.105662

Cited by 4 publications

(2 citation statements)

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“…Thus, the droplet density must be analyzed by also taking into account that there is a difference in spray concentration (Cunha et al 2010b). Nascimento et al (2021), in a study with different application volume rates in the spraying of systemic fungicide to control Asian soybean rust, found that an increase in application volume rate re sulted in an increase in spray coverage and droplet density in three thirds of the plants. An application volume rate of up to 50 l • ha -1 did not negatively affect Asian rust control and soybean plant grain yield.…”

Section: Discussionmentioning

confidence: 99%

Journal of Plant Protection Research

Nascimento,

Parreira,

Milagres

et al. 2022

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An efficient application of phytosanitary products depends, among other factors, on a good selection of nozzles and the application volume rate of the solution used. Thus, the objective of this work was to evaluate the efficiency of different models of hydraulic tips and application volume rates on spray coverage on targets positioned in the upper, middle and lower thirds of corn plants. The application volume rates evaluated were: 50 l • ha −1 ; 100 l • ha −1 ; 150 l • ha −1 ; 200 l • ha −1 ; 300 l • ha −1 and 400 l • ha −1 . The following nozzles were used: TT 11001, TTJ60 11002, TXA 8003, 30HCX 12, GRD120 02 and GAT11002. Appli cations were carried out in phenological stages V6-V7 of corn plants. There was a directly proportional relationship between an increase in application volume rate and the levels of spray coverage and droplet density in the three thirds of corn plants. The application vol ume rate evaluated, except for 50 l • ha −1 in the lower third, provided a number of droplets compatible with the literature recommendations for the application of systemic fungicides. All tips evaluated provided a number of droplets compatible with the recommendations in the literature for the application of systemic fungicides, therefore, they can be recom mended for use in spraying on corn crops.

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Section: Discussionmentioning

confidence: 99%

Journal of Plant Protection Research

Nascimento,

Parreira,

Milagres

et al. 2022

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“…where control is reliant on repeated applications of expensive fungicides to curb infections that would otherwise raze the crop (Nascimento et al 2021). Additionally, climate change alters environmental pressures and management practices, which can change disease distribution and severity.…”

Section: Introductionmentioning

confidence: 99%

Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions

Holan

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Application of a U-Net Neural Network to the Puccinia sorghi–Maize Pathosystem

Holan,

White,

Whitham

2024

Phytopathology®

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Phenotyping in phytopathology has become a critical area of research, particularly as pathogen distribution changes and the ability to overcome both innate resistance and control methods develop among pathogen populations. Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual approaches. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyze several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool, or randomly chosen from a subset of disease time course data. As training dataset size increases, models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real world plant pathology questions related to quantification and estimation of plant disease symptoms.

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Spray mixture volume in the control of Asian soybean rust

Cited by 4 publications

References 25 publications

Journal of Plant Protection Research

Journal of Plant Protection Research

Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions

Application of a U-Net Neural Network to the Puccinia sorghi–Maize Pathosystem

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