2. Preliminaries

Now let’s begin by reviewing the fundamental definitions of Atangana–Baleanu fractional operators.

Definition 1. Let $f \in C^{1}(\alpha, \beta ), \alpha \lt \beta$ , be a function, and let $\sigma \in [0, 1]$ . The ABC fractional derivative of order $\sigma$ is given by [Reference Singh43–Reference Sujaritpong, Yoo-Kong and Bhadola45]

(1) \begin{align} ^{ABC}_{\alpha }D_{t}^{\sigma }f(t)=\frac{G(\sigma )}{1-\sigma }\int _{\alpha }^{t}\frac{df}{dn}E_{\sigma }\left [{-}\frac{\sigma }{1-\sigma }(t-n)^{\sigma }\right ]dn, \end{align}

where $G(\sigma )$ is the normalisation function given by $G(\sigma )=1-\sigma +\frac{\sigma }{\Gamma (\sigma )}$ , characterised by $G(0) = G(1) = 1$ , and the Mittag-Leffler function $E_{\sigma }(z)$ with $\mathbb{C}$ the set of the complex number is given by

(2) \begin{align} E_{\sigma }(z)=\sum _{b=0}^{\infty }\frac{z^{b}}{\Gamma (1+\sigma b)}, \sigma, b \in \mathbb{C}, \mathbb{R}(\sigma )\gt 0. \end{align}

Definition 2. The ABC fractional integral of the function $f \in C^{1}(\alpha, \beta )$ is given by [Reference Singh, Srivastava, Hammouch and Nisar44, Reference Sujaritpong, Yoo-Kong and Bhadola45]

(3) \begin{align} ^{AB}_{\alpha }I_{t}^{\sigma }f(t)=\frac{1-\sigma }{G(\sigma )}f(t)+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\int _{\alpha }^{t}f(n)(t-n)^{\sigma -1}dn. \end{align}

Lemma 1. [Reference Sweilam, Al-Mekhlafi and Baleanu46]: The ABC fractional derivative and ABC fractional integral of the function $f \in C^{1}(\alpha, \beta )$ satisfies the Newton–Leibniz equality

\begin{equation*} ^{AB}_{\alpha }I_{t}^{\sigma }\left (^{ABC}_{\alpha }D_{t}^{\sigma }f(t)\right )=f(t)-f(\alpha ) . \end{equation*}

Lemma 3. [Reference Tilahun, Wolle and Tofik48]: For two functions $f,g \in \mathbb{K}^{1}(\alpha, \beta ), \alpha \lt \beta$ , the AB fractional derivative satisfies the following inequality:

\begin{equation*} \Vert ^{ABC}_{\alpha }D_{t}^{\sigma }f(t)-^{ABC}_{\alpha }D_{t}^{\sigma }g(t) \Vert \leq \mathbb {K}\Vert \,f(t)-g(t) \Vert . \end{equation*}

Then, next, we will proceed to the formulation of the model.

3. Formulation of the model

In this work, we partitioned the CBD model into coffee berry and vector populations. There are susceptible and infected coffee berry subcategories in the total coffee berry population ( $N_{c}(t))$ . Susceptible coffee berry is denoted by $S_{c}$ , and infected coffee berry is denoted by $I_{c}$ . This is defined as $N_{c}(t)=S_{c}+I_{c}$ . There are susceptible and infected subclasses in the total vector population $(N_{v}(t))$ . The susceptible vector is represented by $S_{v}$ , and the infected vector is represented by $I_{v}$ . This is defined as $N_{v}(t)=S_{v}+I_{v}$ . The model took into account the recruitment rate of susceptible vectors $r_{2}$ and the shift to infected vectors $(I_{v})$ with $\beta _{2}$ rate after eating ill plants or coffee berry. The susceptible coffee berry $(S_{c})$ is also replanted at a rate of $r r_{1}$ , and the diseases spread to coffee berry, and when infected vectors $(Y_{v})$ eat susceptible cotton $(S_{c})$ , the diseases propagate to coffee berry at a rate of $\beta _{1}$ . Coffee berry, once infected, never recovers and produces no or extremely low yields. To regulate the illness, the parameter $\gamma$ is the induced death rate and is the elimination rate of infected coffee berry from uninfected coffee berry. Furthermore, $d$ and $\mu$ are the natural death rates of coffee berry and vector population, respectively.

The following governing equations are given based on the model’s assumptions and flow chart in Figure 2:

(4) \begin{align} \frac{dS_{c}}{dt}&=r_{1}-\beta _{1}S_{c}I_{v} - dS_{c}, \nonumber \\[3pt] \frac{dI_{c}}{dt}&=\beta _{1}S_{c}I_{v} - (d+\gamma )I_{c}, \nonumber \\[3pt] \frac{dS_{v}}{dt}&=r_{2}-\beta _{2}I_{c}S_{v} - \mu S_{v},\nonumber \\[3pt] \frac{dI_{v}}{dt}&=\beta _{2}I_{c}S_{v}-\mu I_{v}. \end{align}

Figure 2.

Flow chart of the model.

Now, replacing the $\frac{d}{dt}$ in the system (4) with $^{ABC}_{0}D_{t}^{\sigma }$ , we obtain the AB derivative that is described by the system of differential equations given in equation (5).

(5) \begin{align} ^{ABC}_{0}D_{t}^{\sigma }S_{c}(t)&= r_{1}-\beta _{1}S_{c}I_{v} - dS_{c}, \nonumber \\[3pt] ^{ABC}_{0}D_{t}^{\sigma }I_{c}(t)&=\beta _{1}S_{c}I_{v} - (d+\gamma )I_{c}, \nonumber \\[3pt] ^{ABC}_{0}D_{t}^{\sigma }S_{v}(t)&=r_{2}-\beta _{2}I_{c}S_{v} - \mu S_{v},\nonumber \\[3pt] ^{ABC}_{0}D_{t}^{\sigma }I_{v}(t)&=\beta _{2}I_{c}S_{v}-\mu I_{v}, \end{align}

with initial conditions

(6) \begin{align} S_{c}(0) \gt 0, I_{c}(0) \geq 0, S_{v}(0) \geq 0,I_{v}(0) \geq 0. \end{align}

3.1. Existence and uniqueness of solutions

In this subsection, we shall apply some basic results from fixed point theory to the model (1), in order to establish the existence and uniqueness of the solution. The model (1) is rewritten in the following form:

(7) \begin{align} \begin{cases} ^{ABC}_{\alpha }D_{t}^{\sigma }G(t) & =g(t, G(t)), \\[3pt] g(0) & =g_{0}. \end{cases} \end{align}

where the vector $g=(S_{c}, I_{c},S_{v}, I_{v})$ represents the compartments of the model and $g$ denotes a continuous vector defined as follows:

(8) \begin{align} \texttt{g} = \left ( \begin{array}{c} g_{1}\\[3pt] g_{2}\\[3pt] g_{3}\\[3pt] g_{4} \end{array} \right ) = \left ( \begin{array}{c} r_{1}-\beta _{1}S_{c}I_{v} - dS_{c}\\[3pt] \beta _{1}S_{c}I_{v} - (d+\gamma )I_{c}\\[3pt] r_{2}-\beta _{2}I_{c}S_{v} - \mu S_{v}\\[3pt] \beta _{2}I_{c}S_{v}-\mu I_{v} \end{array} \right ). \end{align}

The initial condition of the variables of the model is denoted by $g(0)=(S_{c}(0), I_{c}(0), S_{v}(0),I_{v}(0))$ . Furthermore, $G$ satisfies the Lipschitz condition as given by Lemma3:

(9) \begin{align} \Vert g(t,g_{1}(t)) -g(t,g_{2}(t))\Vert \leq K\Vert g_{1}(t)- g_{2}(t) \Vert . \end{align}

The existence of the solution for model equation (5) is investigated in the following theorem:

Theorem 1. There exists a unique solution in $C^{1}([0, T])$ to the initial value problem $t\in [0, T]$ given that equation ( 9 ) and

(10) \begin{align} \left (\frac{(1-\sigma )K}{G(\sigma )}+\frac{\sigma K}{G(\sigma )\Gamma (\sigma +1)}T_{\max }^{\sigma }\right )\lt 1. \end{align}

are satisfied.

Proof. Applying ABC fractional integral on both sides of equation (18), we obtain

(11) \begin{align} g(t)=g_{0}+\frac{1-\sigma }{G(\sigma )}g(t,g(t))+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\int _{0}^{t}(t-\tau )^{\sigma -1}g(t,g(t))d\tau . \end{align}

Let $M=(0, K)$ . Then, define the operator $\mathcal{F}\,:\,C(M,\mathbb{R}^{4})\rightarrow C(M,\mathbb{R}^{4})$ by

(12) \begin{align} \mathcal{F}[g(t)]=g_{0}+\frac{1-\sigma }{G(\sigma )}g(t,g(t))+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\int _{0}^{t}(t-\tau )^{\sigma -1}g(t,g(t))d\tau . \end{align}

It implies that $g(t)=\mathcal{F}[g(t)]$ . The supremum norm on $M.\Vert .\Vert _{M}$ is given by

(13) \begin{align} \Vert g(t)\Vert _{M}=^{\sup }_{t\in M}\Vert g(t)\Vert, g(t)\in C. \end{align}

Clearly, $C(M,\mathbb{R}^{4})$ equipped with $\Vert .\Vert _{M}$ is a Banach space. Also, the following inequality holds

(14) \begin{align} \Vert \int _{0}^{t}B(t,\tau )g(\tau )d\tau \Vert \leq \Vert B(t,\tau )\Vert _{M}\Vert g(t)\Vert _{M}, \end{align}

with $g(t)\in C(M,\mathbb{R}^{4}), B(t,\tau )\in C(M^{2},\mathbb{R})$ such that $|B(t,\tau )|_{M}=^{\sup }_{t, \tau \in M}|B(t,\tau )|$ . By applying equation (9), we have that

(15) \begin{align} \Vert \mathcal{F}(g_{1}(t)) -\mathcal{F}(g_{2}(t))\Vert _{M}&\leq \Vert \frac{1-\sigma }{G(\sigma )}\left (g(t,g_{1}(t))-g(t,g_{2}(t))\right )\nonumber \\[3pt] &+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\int _{0}^{t}(t-\tau )^{\sigma -1}\left (g(\tau, g_{1}(\tau ))-g(\tau, g_{2}(\tau ))\right )d\tau \Vert, \nonumber \\[3pt] &\leq \frac{1-\sigma }{G(\sigma )}K\Vert g_{1}(t)-g_{2}(t) \Vert + \frac{\sigma K}{G(\sigma )\Gamma (\sigma )}\int _{0}^{t}(t-\tau )^{\sigma -1}\Vert g_{1}(t)-g_{2}(t) \Vert d\tau, \nonumber \\[3pt] &\leq \frac{1-\sigma }{G(\sigma )}K \sup |_{g\in [0, T]}\Vert g_{1}(t)-g_{2}(t) \Vert \\[-10pt] \nonumber \end{align}

(16) \begin{align} &+\frac{\sigma K}{G(\sigma )\Gamma (\sigma )}\left (\int _{0}^{t}(t-\tau )^{\sigma -1}\right )d\tau \sup |_{g\in [0, T]}\Vert g_{1}(\tau )-g_{2}(\tau ) \Vert, \nonumber \\[3pt] & \leq \left (\frac{(1-\sigma )K}{G(\sigma )}+\frac{\sigma K T_{\max }^{\sigma }}{G(\sigma )\Gamma (\sigma +1)}\right )\Vert g_{1}(t)-g_{2}(t) \Vert . \\[14pt] \nonumber\end{align}

Consequently, if the condition in equation (10) is satisfied, then $\Vert \mathcal{F}([g_{1}(t)]) - \mathcal{F}([g_{2}(t)]) \Vert \lt \Vert g_{1}(t)-g_{2}(t) \Vert$ and the operator $\mathcal{F}$ become a contraction. Hence, $\mathcal{F}$ has a unique fixed point, which is a solution to the initial value problem in (18) and thus a solution to the system of equation (5).

Theorem 2. For the initial condition given in ( 6 ), the solution of the system of equation ( 5 ) is non-negative and bounded for $t\gt 0$ in the region

(17) \begin{align} \Omega =\Omega _{c}\times \Omega _{v}=\left \{(S_{c},I_{c}, S_{v},I_{v})\in \mathbb{R}_{+}^{4}\,:\, N_{c}\leq \frac{r_{1}}{d},N_{v}\leq \frac{r_{1}}{\mu } \right \}. \end{align}

Proof. First, we shall prove that $S_{c}(t)\gt 0$ for all $t \geq 0$ . We assume that $S_{c}(t)\geq 0$ is not true. Then there exists at $\tau _{1}\gt 0$ , such that

(18) \begin{align} \begin{cases} S_{c}(t)\gt 0 & for \hspace{0.25cm}t_{0}\leq t\lt \tau _{1}, \\[3pt] S_{c}(t)=0 & for\hspace{0.25cm} t=\tau _{1},\\[3pt] S_{c}(t)\lt 0 & for\hspace{0.25cm} \tau _{1}\lt t\lt \tau _{1} + \varepsilon\ for\ \varepsilon \gt 0. \end{cases} \end{align}

Then, from the first system of equation (5), we have

(19) \begin{align} ^{ABC}_{0} D^{\sigma }_{t}S_{c}|_{S_{c}=0}=r_{1}\geq 0. \end{align}

Thus, using Lemma2, for all $0\lt \varepsilon \lt \lt 1$ , we have $S_{c}\left (\tau _{1}+\varepsilon \right )=S_{c}(\tau _{1})+\frac{1}{\sigma }\frac{d^{\sigma }}{dt^{\sigma }}S_{c}(t)\varepsilon ^{\sigma }$ . Hence, $S_{c}(\tau _{1}+\varepsilon )\gt 0$ contradicts our assumption that $S_{c}(t)\lt 0$ for $\tau _{1}\lt t\lt \tau _{1}+\varepsilon$ for $\varepsilon \gt 0$ . As a result, we obtain $S_{c}(t)\geq 0$ for all $t \geq 0$ . Similarly, we have $I_{c}(t)\geq 0, S_{v}(t)\geq 0, I_{v}(t)\geq 0,$ for all $t \geq 0$ . It follows that each of the solutions of (5) is non-negative and remains in $\mathbb{R}_{+}^{4}$ , and hence, the set $\Omega$ defined in (17) is positively invariant for model (5).

Eventually, to demonstrate the boundedness of the fractional model’s solutions (5), given that all of the parameters are positive, we continue by adding up all of the model’s equations for both cotton and vector population, which yields

(20) \begin{align} &^{ABC}_{0} D^{\sigma }_{t}N_{c}=r_{1}-d N_{c}-\gamma I_{c}\leq r_{1}-d N_{c}, \\[-6pt] \nonumber \end{align}

(21) \begin{align} &^{ABC}_{0} D^{\sigma }_{t}N_{v}= r_{2}-\mu N_{v}. \\[12pt] \nonumber \end{align}

Now, using the Laplace transform and simplifying equations (20) and (21), we get

(22) \begin{align} N_{c}(t)\leq & \left (\frac{G(\sigma )}{G(\sigma )+(1-\sigma )d}N_{c}(0)+\frac{(1-\sigma )r_{1}}{G(\sigma )+(1-\sigma )d}\right )E_{\sigma, 1}\left (-c_{1}t^{\sigma }\right ) \nonumber \\[3pt] &+\frac{\sigma r_{1}}{G(\sigma )+(1-\sigma )d}E_{\sigma, \sigma +1}\left (-c_{2}t^{\sigma }\right ), \\[-6pt] \nonumber \end{align}

(23) \begin{align} N_{v}(t)\leq & \left (\frac{G(\sigma )}{G(\sigma )+(1-\sigma )\mu }N_{v}(0)+\frac{(1-\sigma )r_{2}}{G(\sigma )+(1-\sigma )\mu }\right )E_{\sigma, 1}\left (-c_{2}t^{\sigma }\right )\nonumber \\[3pt] &+\frac{\sigma r_{2}}{G(\sigma )+(1-\sigma )\psi }E_{\sigma, \sigma +1}\left (-c_{2}t^{\sigma }\right ), \\[12pt] \nonumber \end{align}

where $c_{1}=\frac{\sigma d}{G(\sigma )\,+\,(1-\sigma )d}$ and $c_{2}=\frac{\sigma \mu }{G(\sigma )\,+\,(1-\sigma )\mu }$ with $E_{\sigma, c_{1}}$ and $E_{\sigma, c_{2}}$ constitute the two parameter Mittag-Leffler function and due to its asymptotic nature that leads to the conclusion that $N_{c}(t)\leq \frac{r_{1}}{d}$ and $N_{v}(t)\leq \frac{r_{2}}{\mu } as t \rightarrow \infty$ . Hence, (17) is the biologically feasible region of model (5).

4. Equilibrium points of the model

4.1. The coffee berry disease-free equilibrium points of the model $(E_{0})$

The CBD-free equilibrium points $(E_{0})$ of the model are stationary solutions with no diseases. It is obtained by equating equation (5) to zero and using $I_{c} = 0$ and $Y_{v} = 0$ . Then, $E_{0}$ of our model equation (5) is given by

(24) \begin{align} E_{0} = (S_{c}^{0}, I_{c}^{0}, S_{v}^{0}, I_{v}^{0}) = \left (\frac{r_{1}}{d}, 0, \frac{r_{2}}{\mu }, 0\right ) . \end{align}

4.2. The basic reproduction number $(R_{0})$

The basic reproduction number $(R_{0})$ counts the estimated number of secondary infections that result from one newly infected coffee berry delivered directly into a susceptible population. We can obtain $R_{0}$ of the system of equation (5) using the next-generation matrix method as described by [Reference Van den Driessche and Watmough50]. It can be determined by rewriting the system of equation (5) starting with newly infective classes and using the next-generation matrix method:

(25) \begin{align} ^{ABC}_{0} D^{\sigma }_{t}I_{c} &=\beta _{1}S_{c}I_{v} - (d+\gamma ) I_{c}, \\[-6pt] \nonumber \end{align}

(26) \begin{align} ^{ABC}_{0} D^{\sigma }_{t}I_{v} &=\beta _{2}I_{c}S_{v}-\mu I_{v}. \\[12pt] \nonumber \end{align}

Then, we consider

(27) \begin{align} \boldsymbol{f} = \left ( \begin{array}{c} \beta _{1}S_{c}I_{v}\\[3pt] \beta _{2}I_{c}S_{v} \end{array} \right ), \boldsymbol{v} = \left ( \begin{array}{c} (d+\gamma ) I_{c}\\[3pt] \mu I_{v} \end{array} \right ) \\[-10pt] \nonumber \end{align}

Now, the Jacobian matrix of $\boldsymbol{f}$ and $\boldsymbol{v}$ with respect to $I_{c}$ and $I_{v}$ at CBD-free equilibrium point $E_{0}=\left (\frac{r_{1}}{d}, 0, \frac{r_{2}}{\mu }, 0\right )$ is

(28) \begin{align} \texttt{F} = \left ( \begin{array}{c@{\quad}c} 0 & \frac{\beta _{1}r_{1}}{d}\\[3pt] \frac{\beta _{2}r_{2}}{\mu } & 0 \end{array} \right ), \texttt{V} = \left ( \begin{array}{c@{\quad}c} d+\gamma & 0\\[3pt] 0 & \mu \end{array} \right ). \end{align}

Then, by the principle of next-generation matrix, basic reproduction number $(R_{0})$ is the dominant eigenvalue of the $FV^{-1}$ or spectral radius of $FV^{-1}$ where

(29) \begin{align} FV^{-1} = \left ( \begin{array}{c@{\quad}c} 0 & \frac{\beta _{1}r_{1}}{d}\\[3pt] \frac{\beta _{2}r_{2}}{\mu } & 0 \end{array} \right ) \left ( \begin{array}{c@{\quad}c} \frac{1}{d+\gamma }& 0\\[3pt] 0 & \frac{1}{\mu } \end{array} \right )= \left ( \begin{array}{c@{\quad}c} 0 & \frac{\beta _{1}r_{1}}{d\mu }\\[3pt] \frac{\beta _{2}r_{2}}{\mu (d+\gamma )} & 0 \end{array} \right ). \end{align}

The characteristic equations of equation (29) becomes

(30) \begin{align} \lambda ^{2}-\frac{m_{1}r_{2}\beta _{1}\beta _{2}}{(d+\gamma )d \mu ^{2}}=0 \implies \lambda =\pm \sqrt{\frac{r_{1}r_{2}\beta _{1}\beta _{2}}{(d+\gamma )d \mu ^{2}}}. \end{align}

Since, $R_{0}$ is the maximum eigenvalues of $FV^{-1}$ or the spectral radius of $FV^{-1}$ . That is,

(31) \begin{align} R_{0}=\max \left \{-\sqrt{\frac{r_{1}r_{2}\beta _{1}\beta _{2}}{(d+\gamma )d \mu ^{2}}},\sqrt{\frac{r_{1}r_{2}\beta _{1}\beta _{2}}{(d+\gamma )d \mu ^{2}}}\right \}. \end{align}

4.3. Coffee berry disease endemic equilibrium point of the model ( $E_{1}$ )

The coffee berry endemic equilibrium point, $E_{1}=(S_{c}^{*},I_{c}^{*},S_{v}^{*},I_{v}^{*} )$ , of the model is the steady-state solution, where coffee berries persist in the population of coffee plants. We can obtain this by equating each system of equation (5) equal to zero, and it is given by

(32) \begin{align} S_{c}^{*}&=\frac{r_{1}(r_{1}\beta _{2}+\mu (d+\gamma ))}{d\mu (d+\gamma )(R_{0}^{2}-1)+d(r_{1}\beta _{2}+\mu (d+\gamma ))}, \\[-2pt] \nonumber \end{align}

(33) \begin{align} I_{c}^{*}&=\frac{r_{1}\mu (R_{0}^{2}-1)}{\mu ((d+\gamma ))(R_{0}^{2}-1)+(r_{1}\beta _{2}+\mu (d+\gamma ))}, \\[-2pt] \nonumber \end{align}

(34) \begin{align} S_{v}^{*}&=\frac{r_{2}((d+\gamma )\mu (R_{0}^{2}-1)+r_{1}\beta _{2}+d\mu )}{\mu ((r_{1}\beta _{2}+d\mu )(R_{0}^{2}-1)+r_{1}\beta _{2}+\mu (d+\gamma ))}, \\[-2pt] \nonumber \end{align}

(35) \begin{align} I_{v}^{*}&= \frac{d\mu (d+\gamma )(R_{0}^{2}-1)}{\beta _{1}(r_{1}\beta _{2}+\mu )(d+\gamma )}. \\[12pt] \nonumber \end{align}

5. Stability analysis of the model

5.1. Local stability of the coffee berry disease-free equilibrium point

The linearisation system of equation (5) at $E_{0}$ can be used to find the local stability of the model at $E_{0}$ .

Theorem 3. Disease-free equilibrium point $(E_{0})$ of the system of equation ( 5 ) is locally asymptotically stable, if $R_{0}\lt 1 .$

Proof. Evaluating the Jacobian matrix of the system of equation (5) at $E_{0}=\left (\frac{r_{1}}{d}, 0, \frac{r_{2}}{\mu }, 0\right )$ is

(36) \begin{align} J(E_{0})=\left ( \begin{array}{c@{\quad}c@{\quad}c@{\quad}c} -d & 0 &0 & -\frac{\beta _{1}r_{1}}{d}\\[3pt] 0 & -(d+\gamma ) & 0 & \frac{\beta _{1}r_{1}}{d}\\[3pt] 0 & -\frac{\beta _{2}r_{2}}{\mu }& -\mu & 0\\[3pt] 0 & \frac{\beta _{2}r_{2}}{\mu } & 0 & -\mu \end{array} \right ). \end{align}

The corresponding characteristic equation of the Jacobian matrix of equation (36) at $E_{0}$ is given by $|J(E_{0})-\lambda I_{4}| =0$ . That is,

(37) \begin{align} \left (-d-\lambda \right )\left (-\mu -\lambda \right )(\lambda ^{2}+d_{1}\lambda +d_{2})=0. \end{align}

where

(38) \begin{align} &d _{1} = d+\gamma +\mu, \nonumber \\[3pt] &d _{2} = (d+\gamma )\mu -\frac{r_{1}r_{2}\beta _{1}\beta _{2}}{d\mu }=(d+\gamma )\mu \left [1- \frac{r_{1}r_{2}\beta _{1}\beta _{2}}{(d+\gamma )d\mu ^{2}}\right ]=(d+\gamma )\mu (1-R_{0}^{2}). \end{align}

Clearly, from equation (37), we observe that

(39) \begin{align} \lambda _{1} = -d \lt 0, \lambda _{2} = -\mu \lt 0, \end{align}

and from the last expression of equation (37), that is,

(40) \begin{align} \lambda ^{2}+d_{1}\lambda +d_{2}=0, \end{align}

by using the Routh–Hurwitz criteria, equation (40) has a strictly negative real root if $d_{1}\gt 0$ and $d_{2}\gt 0$ . Clearly, we observe that $d_{1}= d+\gamma +\mu \gt 0$ and

(41) \begin{align} d_{2}= (d+\gamma )\mu (1-R_{0}^{2})\gt 0, \end{align}

if $(1-R_{0}^{2})\gt 0$ . That is, $R_{0}^{2}\lt 1$ implies that $R_{0}\lt 1$ . As a result, our model equation (5) at $E_{0}$ offers all eigenvalues with a negative real part, and so it is locally asymptotically stable if $R_{0}\lt 1$ .

5.2. Global stability of the coffee berry disease-free equilibrium point

To establish the global stability of disease-free equilibrium point $(E_{0})$ , we need to rewrite the system of equation (5) in the following form:

(42) \begin{align} &\dot{M} = J(M,L),\nonumber \\[3pt] &\dot{L} = P(M,L),\nonumber \\[3pt] &P(M,0)=0, \end{align}

where $ M= (S_{c}, S_{v})\in R^{2}$ represents the number of uninfected classes, while $L = (I_{c},I_{v})\in R^{2}$ represents the number of infected classes, and $E_{0}=(M^{0},0)$ represents the CBD-free equilibrium of this system. The disease-free equilibrium $E_{0}$ is a globally asymptotically stable equilibrium for the model if the following conditions are fulfilled:

1. 1. $\frac{dM}{dt}=J(M,0), M^{*}$ is a globally asymptotically stable.

2. 2. $\frac{dL}{dt}=D_{L}P(M^{*},0)L-P_{1}(M,L), P_{1}(M,L)\geq 0$ , $\forall (M,L)\in \Omega$ .

where $D_{L}P(M^{0},0)$ is an M-matrix and $P(M,L)$ taken in $(I_{c},Y_{v})$ and evaluated at $(M^{0},0)=\left (\frac{r_{1}}{d},\frac{r_{2}}{\mu },0,0\right )$ . If the system of equation (42) satisfies the above conditions, then the following theorem holds.

Theorem 4. The coffee berry disease-free equilibrium point, $E_{0}=(M^{0},0)$ , of the system of equation ( 5 ) is globally asymptotically stable if $R_{0} \leq 1$ and conditions ( 1 ) and ( 2 ) are satisfied.

Proof. From our model of equation (5), we can obtain $J(M,L)$ and $P(M,L)$ :

\begin{equation*} J(M,L)=\left ( \begin {array}{c} r_{1}-\beta _{1}S_{c}I_{v} - d S_{c} \\[3pt] r_{2}-\beta _{2}I_{c}S_{v} - \mu S_{v} \end {array} \right ), \end{equation*}

\begin{equation*} P(M,L)= \left ( \begin {array}{c} \beta _{1}S_{c}I_{v} - (d+\gamma ) I_{c} \\[3pt] \beta _{2}I_{c}S_{v}-\mu I_{v} \end {array} \right ) . \end{equation*}

Now, we consider the reduced system and from condition (1):

(43) \begin{align} &^{ABC}_{0} D^{\sigma }_{t}S_{c}=r_{1} - d S_{c}, \end{align}

(44) \begin{align} &^{ABC}_{0} D^{\sigma }_{t}S_{v}=r_{2}- \mu S_{v}, \\[12pt] \nonumber \end{align}

$M^{0}=\left (\frac{r_{1}}{d}, \frac{r_{2}}{\mu }\right )$ is a globally asymptotically stable equilibrium point for the reduced system $\frac{dM}{dt}=J(M,0)$ . Using the Laplace transform, the solution of equation (43) is $S_{c}(t)=\frac{r_{1}}{d}+\left (S_{c}(0)-\frac{r_{1}}{d}\right )e^{-d t}$ , which approaches $\frac{r_{1}}{d}$ as $t\rightarrow \infty$ , and from equation (44), we obtain $S_{v}(t)=\frac{r_{2}}{\psi }+(S_{v}(0)-\frac{r_{2}}{\mu })e^{-\mu t}$ , which approaches $\frac{r_{2}}{\mu }$ as $t\rightarrow \infty$ . Note that this asymptomatic dynamics is independent of the initial conditions in $\Omega$ ; therefore, the convergence of the solutions of the reduced system (43) and (44) is global in $\Omega$ . Now, we compute

(45) \begin{align} D_{L}P(M^{0},0)= \left ( \begin{array}{c@{\quad}c} -(d+\gamma ) &\frac{r_{1}\beta _{1}}{d} \\[3pt] \frac{r_{2}\beta _{2}}{\mu } &-\mu \end{array} \right ). \end{align}

Then, $P(M,L)$ can be written as

(46) \begin{align} P(M,L)=D_{L}P(M^{*},0)L-P_{1}(M,L), \end{align}

and we want to show $P_{1}(M,L)$ , which is obtained as

(47) \begin{align} P_{1}(M,L)= \left ( \begin{array}{c} \beta _{1}I_{v}\left (\frac{r_{1}}{d}-S_{c}\right ) \\[3pt] \beta _{2}I_{c}\left (\frac{r_{2}}{\mu }-S_{v}\right ) \end{array} \right ). \end{align}

Here $\frac{r_{1}}{d} \geq S_{c}$ and $\frac{r_{2}}{\mu } \geq S_{v}$ . Hence, it is clear that $P_{1}(M,L) \geq 0$ , $\forall (M,L) \in \Omega$ . Thus, this proves that $E_{0}$ is globally asymptotically stable when $R_{0} \leq 1$ .

5.3. Local stability of the coffee berry endemic equilibrium point

We used the Jacobian stability approach to prove the local stability of the disease endemic equilibrium state in this section.

Theorem 5. When $R_{0} \gt 1$ , the model’s endemic equilibrium point, $E_{1}$ , is locally asymptotically stable.

Proof. The local stability of the coffee berry endemic equilibrium $(E_{1})$ is determined based on the signs of the eigenvalues of the Jacobian matrix, which is computed at $E_{1}$ . Now, the Jacobian matrix of our model at $E_{1}$ is given by

(48) \begin{align} J = \left ( \begin{array}{c@{\quad}c@{\quad}c@{\quad}c} -(\beta _{1}I_{v}^{*}+d)&0 & 0 & -\beta _{1}S_{c}^{*}\\[3pt] \beta _{1}I_{v}^{*} & -(d+\gamma ) & 0 & \beta _{1}S_{c}^{*}\\[3pt] 0 & -\beta _{2}S_{v}^{*} & -(\beta _{2}I_{c}^{*}+\mu ) & 0\\[3pt] 0 & \beta _{2}S_{v}^{*} & \beta _{2}I_{c}^{*} & -\mu \end{array} \right ). \end{align}

The corresponding characteristic equation of the Jacobian matrix of equation (48) at $E_{1}$ is $|J(E_{1})- \lambda I_{4}| =0$ . That is,

(49) \begin{align} P(\lambda ) = f_{4} \lambda ^{4}+f_{3} \lambda ^{3}+f_{2} \lambda ^{2}+f_{1} \lambda + f_{0}, \end{align}

where

(50) \begin{align} f_{4}=\,&1,f_{3}=2d+2\mu +\beta _{1}I_{v}^{*}+\beta _{2}I_{c}^{*}, \nonumber \\[3pt] f_{2} = \,&d\mu +(d+\mu )(d+\gamma +\mu )(d+\gamma )\mu +(2\mu +d)\beta _{1}I_{v}^{*}+(2d+\mu )\beta _{2}I_{c}^{*}+\beta _{1}\beta _{2}(I_{c}^{*}-S_{c}^{*}I_{v}^{*}),\nonumber \\[3pt] f_{1}= \, &\mu d(d+\gamma +\mu )+(d+\mu )(d+\gamma )\mu +(d\mu +(d+\mu )(d+\gamma ))\beta _{2}I_{c}^{*}+(d+\gamma +\mu )\mu \beta _{1}I_{v}^{*}\nonumber \\[3pt] &+(d+\mu )\beta _{1}\beta _{2}I_{c}^{*}I_{v}^{*}-\beta _{1}\beta _{2}(d+\mu )S_{c}^{*}S_{v}^{*}, \nonumber \\[3pt] f_{0} = \, & \mu \beta _{1}\beta _{2}(d+\gamma ) I_{c}^{*}S_{v}^{*}+\beta _{1}\mu ^{2}(d+\gamma ) I_{v}^{*}+\mu \beta _{1}\beta _{2}(d+\gamma ) I_{c}^{*}+d \mu ^{2}(d+\gamma )-d\mu \beta _{1}\beta _{2}S_{c}^{*}S_{v}^{*}. \end{align}

Using the Routh–Hurwitz criterion, all roots of a characteristic polynomial have negative real parts if and only if $ f_{4}\gt 0, f_{3}\gt 0, f_{2}\gt 0, f_{1}\gt 0,f_{0}\gt, f_{3}f_{2}\gt f_{1}$ and $f_{1}f_{2}f_{3}\gt f_{0}f_{3}^{2}+f_{1}^{2}$ for $R_{0} \gt 1$ . Therefore, $E_{1}$ is locally asymptotically stable if $R_{0} \gt 1$ .

6. Numerical solution of the model

In this section, we develop a numerical scheme for model (5) using the Toufik–Atangana rule detailed in [Reference Toufik and Atangana49]. Now from the first equation of (5), we have

(51) \begin{align} &^{ABC}_{0} D^{\sigma }_{t}S_{c} =F_{1}(t,S_{c}(t)),\nonumber \\[3pt] &S_{c}(0)=S_{c\hspace{0.05cm}0}. \end{align}

Based on (11), we obtain the solution for (51) given in (52):

(52) \begin{align} S_{c}(t)=S_{c}(0)+\frac{1-\sigma }{G(\sigma )}F_{1}(t,S_{c}(t))+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\int _{0}^{t}F_{1}(\rho, S_{c}(\rho ))(t-\rho )^{\sigma -1}d\rho . \end{align}

Applying Lagrange’s interpolation polynomial on the interval $[t_{k}, t_{k+1}]$ to the equality $F_{1}(w,S_{c}(w))=r_{1}-\beta _{1}S_{c}(w)Y_{v}(w)-dS_{c}(w)$ leads to

(53) \begin{align} S_{c}k\approx &\frac{1}{h}\left [(w-t_{k-1})F_{1}(t_{k},S_{c}(t_{k}),I_{c}(t_{k}),S_{v}(t_{k}),I_{v}(t_{k})) \right ]\nonumber \\[3pt] &-\frac{1}{h}\left [(w-t_{k})F_{1}(t_{k-1},S_{c}(t_{k-1}),I_{c}(t_{k-1}),S_{v}(t_{k-1}),I_{v}(t_{k-1}) )\right ], \end{align}

where $h=t_{k}-t_{k-1}$ . Now, substituting equation (53) into equation (52), we get

(54) \begin{align} S_{c}(t_{s+1})= \, &S_{c}(0)+\frac{1-\sigma }{G(\sigma )}F_{1}(t_{k},S_{c}(t_{k}),I_{c}(t_{k}),S_{v}(t_{k}),I_{v}(t_{k}))+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\nonumber \\[3pt] &\sum _{j=1}^{s}\frac{F_{1}(t_{j},S_{c}(t_{j}),I_{c}(t_{j}),S_{v}(t_{j}),I_{v}(t_{j}))}{h}\int _{t_{j}}^{t_{j+1}}(w-t_{j-1})(t_{s+1}-w)^{\sigma -1}dw +\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\nonumber \\[3pt] &\sum _{j=1}^{s}-\frac{F_{1}(t_{j-1},S_{c}(t_{j-1}),I_{c}(t_{j-1}),S_{v}(t_{j-1}),I_{v}(t_{j-1}))}{h}\int _{t_{j}}^{t_{j+1}}(w-t_{j-1})(t_{s+1}-w)^{\sigma -1}dw, \nonumber \\[3pt] = \, & S_{c}(0)+\frac{1-\sigma }{G(\sigma )}F_{1}(t_{s},S_{c}(t_{s}),I_{c}(t_{s}),S_{v}(t_{s}),I_{v}(t_{s}))+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\nonumber \\[3pt] &\sum _{j=1}^{s}\frac{F_{1}(t_{j},S_{c}(t_{j}),I_{c}(t_{j}),S_{v}(t_{j}),I_{v}(t_{j}))}{h}M_{j-1}+{G(\sigma )\Gamma (\sigma )}\nonumber \\[3pt] &\sum _{j=1}^{s}-\frac{F_{1}(t_{j-1},S_{c}(t_{j-1}),I_{c}(t_{j-1}),S_{v}(t_{j-1}),I_{v}(t_{j-1}))}{h}M_{j}, \end{align}

where

(55) \begin{align} M_{j-1}=&\int _{t_{j}}^{t_{j}+1}(w-t_{j-1})(t_{s+1}-w)^{\sigma -1}dw= -\frac{1}{\sigma }\left [(t_{j+1}-t_{j-1})(t_{s+1}-t_{j+1})^{\sigma }-(t_{j}-t_{j-1})(t_{s+1}-t_{j})^{\sigma }\right ]\nonumber \\[3pt] &-\frac{1}{\sigma (\sigma +1)}\left [(t_{s+1}-t_{j+1})^{\sigma +1}(t_{s+1}-t_{j+1})^{\sigma }-(t_{s+1}-t_{j})^{\sigma +1}\right ], \\[-2pt] \nonumber \end{align}

(56) \begin{align} M_{j}=&\int _{t_{j}}^{t_{j}+1}(w-t_{j-1})(t_{s+1}-w)^{\sigma -1}dw= -\frac{1}{\sigma }\left [(t_{j+1}-t_{j-1})(t_{s+1}-t_{j+1})^{\sigma }\right ]\nonumber \\[3pt] &-\frac{1}{\sigma (\sigma +1)}\left [(t_{s+1}-t_{j+1})^{\sigma +1}-(t_{s+1}-t_{j})^{\sigma +1}\right ]. \\[12pt] \nonumber \end{align}

Moreover, substituting $t_{j}=jh$ into equations (55) and (56) gives

(57) \begin{align} M_{j-1}=&\frac{h^{\sigma +1}}{\sigma (\sigma +1)}\left [(s+1-j)^{\sigma }(s-j+2+\sigma )-(s-j)^{\sigma }(s-j+2+2\sigma )\right ], \\[-2pt] \nonumber \end{align}

(58) \begin{align} M_{j}=&\frac{h^{\sigma +1}}{\sigma (\sigma +1)}\left [(s+1-j)^{\sigma +1}-(s-j)^{\sigma }(s-j+1+\sigma )\right ]. \\[12pt] \nonumber \end{align}

Finally, we can express equation (54) in terms of equations (57) and (58) as follows:

(59) \begin{align} S_{c}(t_{s+1}) = \, & S_{c}(t_{0})+\frac{1-\sigma }{G(\sigma )}F_{1}(t_{s},S_{c}(t_{s}),I_{c}(t_{s}),S_{v}(t_{s}),I_{v}(t_{s}))+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\nonumber \\[3pt] &\times \sum _{j=1}^{s}\frac{F_{1}(t_{j},S_{c}(t_{j}),I_{c}(t_{j}),S_{v}(t_{j}),I_{v}(t_{j}))}{\Gamma (\sigma +2)}\nonumber \\[3pt] &\times h^{\sigma }\left [(s+1-j)^{\sigma }(s-j+2+\sigma )-(s-j)^{\sigma }(s-j+2+2\sigma )\right ]+\frac{\sigma }{G(\sigma )\Gamma (\sigma )} \nonumber \\[3pt] &\times \sum _{j=1}^{s}-\frac{F_{1}(t_{j-1},S_{c}(t_{j-1}),I_{c}(t_{j-1}),S_{v}(t_{j-1}),I_{v}(t_{j-1}))}{\Gamma (\sigma +2)}\\[3pt] &\times h^{\sigma } \left [(s+1-j)^{\sigma +1}-(s-j)^{\sigma }(s-j+1+\sigma )\right ]. \nonumber \end{align}

In the same way, we have the following equations for the remaining state variables:

(60) \begin{align} I_{c}(t_{s+1}) = \,&I_{c}(t_{0})+\frac{1-\sigma }{G(\sigma )}F_{2}(t_{s},S_{c}(t_{s}),I_{c}(t_{s}),S_{v}(t_{s}),I_{v}(t_{s}))+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\nonumber \\[3pt] &\times \sum _{j=1}^{s}\frac{F_{2}(t_{j},S_{c}(t_{j}),I_{c}(t_{j}),S_{v}(t_{j}),I_{v}(t_{j}))}{\Gamma (\sigma +2)}\nonumber \\[3pt] &\times h^{\sigma }\left [(s+1-j)^{\sigma }(s-j+2+\sigma )-(s-j)^{\sigma }(s-j+2+2\sigma )\right ]+\frac{\sigma }{G(\sigma )\Gamma (\sigma )} \nonumber \\[3pt] &\times \sum _{j=1}^{s}-\frac{F_{2}(t_{j-1},S_{c}(t_{j-1}),I_{c}(t_{j-1}),S_{v}(t_{j-1}),I_{v}(t_{j-1}))}{\Gamma (\sigma +2)}\\[3pt] &\times h^{\sigma } \left [(s+1-j)^{\sigma +1}-(s-j)^{\sigma }(s-j+1+\sigma )\right ]. \nonumber \\[-2pt] \nonumber \end{align}

(61) \begin{align} S_{v}(t_{s+1})=\, & S_{v}(t_{0})+\frac{1-\sigma }{G(\sigma )}F_{3}(t_{s},S_{c}(t_{s}),I_{c}(t_{s}),S_{v}(t_{s}),I_{v}(t_{s}))+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\nonumber \\[3pt] &\times \sum _{j=1}^{s}\frac{F_{3}(t_{j},S_{c}(t_{j}),I_{c}(t_{j}),S_{v}(t_{j}),I_{v}(t_{j}))}{\Gamma (\sigma +2)}\nonumber \\[3pt] &\times h^{\sigma }\left [(s+1-j)^{\sigma }(s-j+2+\sigma )-(s-j)^{\sigma }(s-j+2+2\sigma )\right ]+\frac{\sigma }{G(\sigma )\Gamma (\sigma )} \nonumber \\[3pt] &\times \sum _{j=1}^{s}-\frac{F_{3}(t_{j-1},S_{c}(t_{j-1}),I_{c}(t_{j-1}),S_{v}(t_{j-1}),I_{v}(t_{j-1}))}{\Gamma (\sigma +2)}\\[3pt] &\times h^{\sigma } \left [(s+1-j)^{\sigma +1}-(s-j)^{\sigma }(s-j+1+\sigma )\right ]. \nonumber \\[-2pt] \nonumber \end{align}

(62) \begin{align} I_{v}(t_{s+1})= \, & I_{v}(t_{0})+\frac{1-\sigma }{G(\sigma )}F_{4}(t_{s},S_{c}(t_{s}),I_{c}(t_{s}),S_{v}(t_{s}),I_{v}(t_{s}))+\frac{\sigma }{G(\sigma )\Gamma (\sigma )}\nonumber \\[3pt] &\times \sum _{j=1}^{s}\frac{F_{4}(t_{j},S_{c}(t_{j}),I_{c}(t_{j}),S_{v}(t_{j}),I_{v}(t_{j}))}{\Gamma (\sigma +2)}\nonumber \\[3pt] &\times h^{\sigma }\left [(s+1-j)^{\sigma }(s-j+2+\sigma )-(s-j)^{\sigma }(s-j+2+2\sigma )\right ]+\frac{\sigma }{G(\sigma )\Gamma (\sigma )} \nonumber \\[3pt] &\times \sum _{j=1}^{s}-\frac{F_{4}(t_{j-1},S_{c}(t_{j-1}),I_{c}(t_{j-1}),S_{v}(t_{j-1}),I_{v}(t_{j-1}))}{\Gamma (\sigma +2)}\\[3pt] &\times h^{\sigma } \left [(s+1-j)^{\sigma +1}-(s-j)^{\sigma }(s-j+1+\sigma )\right ]. \nonumber \\[12pt] \nonumber \end{align}

7. Numerical simulation

This section examines the effects of various fractional order values on the system of equation (5). In order to achieve this, we present a number of numerical simulations of the model that make use of a numerical method created by Toufic and Antagana, as shown in equations (59)–(62). The following initial conditions are used in the simulations and analysis: $S_{c}(0)=700, I_{c}(0)= 150, S_{v}(0)= 100, I_{v}(0)= 180$ . The values of the parameters are shown in Table 1.

Table 1.

The parameter values

Figure 3.

Time series plot of state variables for $R_{0}\lt 1$ .

Figure 4.

Time series plot of state variables for $R_{0}\gt 1$ .

Figure 5.

Total number of susceptible and infected coffee berry with different values of $\sigma$ .

Figure 6.

Total number of susceptible and infected vector with different values of $\sigma$ .

Figure 3 depicts how populations of susceptible coffee berries rise asymptotically to the disease-free equilibrium point, while those of infected coffee berries fall asymptotically to that point. The susceptible vector population increases asymptotically to the disease-free equilibrium point, whereas the infected vector population decreases asymptotically to the same equilibrium point. In this case, the disease might ultimately go away in the long run. The fact that $R_{0}=0.0442$ , which is less than one, accounts for the existence of such a condition. This indicates the theorem that the stability of the disease-free equilibrium point occurs when $R_{0} \lt 1$ ; that is, if $R_{0} \lt 1$ , then on average, one infected coffee berry produces less than one newly infectious coffee berry over the course of its disease period. Figure 4 demonstrates how the number of susceptible coffee berry and vector decreases as a result of the influence of infected coffee berry and vector people, after which they join the infected class, leading to an increase in infected coffee berry and vector. As a result, the number of infected coffee berries and vectors is rising, and the disease’s endemic equilibrium point is both present and stable. The fact that $R_{0} = 5.3244$ , which is greater than one, proves that this condition exists. This demonstrates the theorem that disease endemic equilibrium points are stable when $R_{0}\gt 1$ ; that is, if each infected coffee berry and its vectors produce, on average, more than one new infected coffee berry and vector, then the disease can spread in the specified area. Figure 5a demonstrates how decreasing the number of coffee berries $(S_{c})$ that are susceptible results from increasing the fractional order $(\sigma )$ from 0.04 to 1.00. Figure 5b demonstrates how increasing the fractional order $(\sigma )$ from 0.04 to 1.00 results in an increase in the number of infected coffee berries $(I_{c})$ . According to Figure 6a, the number of susceptible vector populations $(S_{v})$ decreases as fractional order $(\sigma )$ , increases from 0.04 to 1.00. The fractional order $(\sigma )$ is shown in Figure 6b to increase from 0.04 to 1.00, which results in an increase in the population of infected vectors $(I_{v})$ . To put it another way, we can infer from Figures 5 and 6 that a reduction in $sigma$ results in a marked decrease in the number of $I_{c}$ and $I_{v}$ cases. The curves for each compartment, $I_{c}$ and $I_{v}$ , also compress as the value decreases from 1.00 to 0.04, as shown in the figures. Therefore, we can say that diseases in $I_{c}$ and $I_{v}$ decrease as one approaches zero from the right.

More specifically, the memory effect of the dynamic system is increased when the derivative order is decreased from 1.00 to 0.04, which causes the infection in each compartment to gradually worsen over time. As a result, we can infer from the results that lowering the derivative’s fractional order significantly slows the growth of crop infections and that lowering it to zero produces no change in rate of change. If the disease’s rate of spread stays the same, the crop will produce. Because of this, the fractional order will be higher than 0. The role of farmer’s experience or knowledge of the history of the disease is also captured by the derivative order. The approximative findings support the notion that fractional order derivatives offer a wealth of dynamics and frequently express biological systems more effectively. The results allow us to draw the conclusion that lowering the derivative’s fractional order significantly slows the growth of crop infections and that lowering it to zero produces no change in rate of change. If there is no change in the disease’s rate, the crop will produce. The fractional order will thus be higher than zero. Additionally, the derivative order captures the significance of farmer’s expertise or familiarity with the disease’s past. The rough results show that fractional order derivatives have a lot of dynamics and have an ability to better represent biological systems.