Optimal control of plant disease model with roguing, replanting, curative, and preventive treatment

Anggriani, Nursanti; Amelia, Rika; Istifadah, Noor; Arumi, D.

doi:10.1088/1742-6596/1657/1/012050

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Epidemic models involving an exposed class are widely known for plant virus propagation [52,53]. Models containing exposed densities have the advantage of not requiring the initial/staring conditions to be presented at an interval equal to the delay, as delay differential equations (DDEs) require.…”

Section: Plant Virus Propagation Model By a Vector: Model Amentioning

confidence: 99%

Reliable numerical treatment with Adams and BDF methods for plant virus propagation model by vector with impact of time lag and density

Anwar

Naz

Shoaib

2022

Front. Appl. Math. Stat.

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Plant disease incidence rate and impacts can be influenced by viral interactions amongst plant hosts. However, very few mathematical models aim to understand the viral dynamics within plants. In this study, we will analyze the dynamics of two models of virus transmission in plants to incorporate either a time lag or an exposed plant density into the system governed by ODEs. Plant virus propagation model by vector (PVPMV) divided the population into four classes: susceptible plants [S(t)], infectious plants [I(t)], susceptible vectors [X(t)], and infectious vectors [Y(t)]. The approximate solutions for classes S(t), I(t), X(t), and Y(t) are determined by the implementation of exhaustive scenarios with variation in the infection ratio of a susceptible plant by an infected vector, infection ratio of vectors by infected plants, plants' natural fatality rate, plants' increased fatality rate owing to illness, vectors' natural fatality rate, vector replenishment rate, and plants' proliferation rate, numerically by exploiting the knacks of the Adams method (ADM) and backward differentiation formula (BDF). Numerical results and graphical interpretations are portrayed for the analysis of the dynamical behavior of disease by means of variation in physical parameters utilized in the plant virus models.

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Section: Plant Virus Propagation Model By a Vector: Model Amentioning

confidence: 99%

Reliable numerical treatment with Adams and BDF methods for plant virus propagation model by vector with impact of time lag and density

Anwar

Naz

Shoaib

2022

Front. Appl. Math. Stat.

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“…Several studies have used mathematical models to study diseases transmission dynamics in plant [12][13][14][15][16][17][18], including co-infection with multiple pathogen and the resulting interactions like cross protection [12] and helper-dependent [18]. Mathematical models have also been used to understand mitigation strategies to curtail the spread of some plant pathogen [17,19]. Thus, we develop several models using differential equations to determine the disease transmission rate in experimental treatments with and without protective layers.…”

Section: Introductionmentioning

confidence: 99%

Cacao sustainability: The case of cacao swollen-shoot virus co-infection

Agusto,

Leite,

Owusu-Ansah

et al. 2024

PLoS ONE

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The cacao swollen shoot virus disease (CSSVD) is among the most economically damaging diseases of cacao trees and accounts for almost 15–50% of harvest losses in Ghana. This virus is transmitted by several species of mealybugs (Pseudococcidae, Homoptera) when they feed on cacao plants. One of the mitigation strategies for CSSVD investigated at the Cocoa Research Institute of Ghana (CRIG) is the use of mild-strain cross-protection of cacao trees against the effects of severe strains. In this study, simple deterministic, delay, and stochastic ordinary differential equation-based models to describe the dynamic of the disease and spread of the virus are suggested. Model parameters are estimated using detailed empirical data from CRIG. The modeling outcomes demonstrate a remarkable resemblance between real and simulated dynamics. We have found that models with delay approximate the data better and this agrees with the knowledge that CSSVD epidemics develop slowly. Also, since there are large variations in the data, stochastic models lead to better results. We show that these models can be used to gain useful informative insights about the nature of disease spread.

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“…Regression analysis in mathematical and statistical models exhibits the advantages of solving a wide range of problems, requiring a moderate amount of data, and being easy to operate. Furthermore, it is most generally adopted to predict a wide variety of diseases [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of the Grade of Leaf Disease in Apple Trees Based on PCA-Logistic Regression Analysis

Xing

Dian

Yin

2023

Forests

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Extensive research suggested that the core of how to use pesticides scientifically is the careful and accurate determination of the severity of crop diseases. The existing grading standards of plant leaf diseases have been excessively singular. Thus, the diseases roughly fall into general and severe grades. To address the above problems, this study considered the effect of the distribution of disease spots, and two evaluation indicators (termed the imbalance degree and main vein distance) were newly added to optimize the grading criteria of apple leaf diseases. Combined with other factors, the grade evaluation indicator was determined through PCA principal component analysis. A gradual multivariate logistic regression algorithm was proposed to evaluate apple leaf disease grade and an optimized apple leaf disease grade evaluation model was built through PCA-logistic regression analysis. In addition, three common apple leaf diseases with a total of 4500 pictures (i.e., black rot, scab, and rust) were selected from several open-source datasets as the subjects of this paper. The object detection algorithm was then used to verify the effectiveness of the new model. As indicated by the results, it can be seen from the loss curve that the loss rate reaches a stable range of around 70 at the epoch. Compared with Faster R-CNN, the average accuracy of Mask R-CNN for the type and grade recognition of apple leaf disease was optimized by 4.91%, and the average recall rate was increased by 5.19%. The average accuracy of the optimized apple leaf disease grade evaluation model was 90.12%, marking an overall increase of 20.48%. Thus, the effectiveness of the new model was confirmed.

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Optimal control of plant disease model with roguing, replanting, curative, and preventive treatment

Cited by 4 publications

References 9 publications

Reliable numerical treatment with Adams and BDF methods for plant virus propagation model by vector with impact of time lag and density

Reliable numerical treatment with Adams and BDF methods for plant virus propagation model by vector with impact of time lag and density

Cacao sustainability: The case of cacao swollen-shoot virus co-infection

The Evaluation of the Grade of Leaf Disease in Apple Trees Based on PCA-Logistic Regression Analysis

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