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Plant diseases pose a significant threat to the sustainability of the environment and global food security. With an increasing population density and the growing demand for plant-based food, the need to address the ongoing plant disease pandemic has become urgent. These issues may be resolved and a more precise and effective approach for early disease detection in smart agriculture can be provided through the use of the embedded systems, Internet of Things (IoT), cloud platforms, machine learning (ML), and deep learning (DL). This paper presents a summary of current work in this field, as well as novel ideas put forth to increase the precision and effectiveness of plant disease detection. In this survey paper, we present (a) a survey on various plant diseases, (b) a method to detect plant diseases using IoT, embedded systems, and cloud platforms that receive and process data in real-time, (c) a ML pipeline for disease identification, and (d) a method to detect plant diseases using deep learning. Framework, dataset, and hyperspectral imaging with DL models for plant disease identification (e). The analysis, challenges of plant disease and pest detection using DL, ML, embedded systems,and the IoT are described in this paper. These databases: ScienceDirect, Springer, IEEE Xplore, MDPI, Hindawi, Frontiers, and others were used in this comprehensive search. Researchers, policymakers, and other stakeholders interested in smart agriculture, plant disease detection, and sustainable food security may find this resource from this study useful. [ABSTRACT FROM AUTHOR]

This paper deals with the problems of robust exponential stability for a class of uncertain discrete-time switched systems with time-varying delay and linear fractional perturbations parametric uncertainties, under arbitrary switching signal on the one hand and by using the Average Dwell Time (ADT) approach on the other hand. Firstly, new sufficient conditions guaranteeing the exponential stability of the nominal considered system are proposed and proved, by constructing an augmented Lyapunov–Krasovskii functional, by using the discrete Wirtinger-based inequality combined with an improved form of Reciprocally Convex Lemma (RCL) and by considering the two types of switching signal mentioned above. Then, and based on the obtained results, new criteria are established to ensure the robust exponential stability for uncertain discrete-time switched systems with interval time-varying delay. The established conditions are dependent on the lower and the upper delay bounds and are expressed in terms of Linear Matrix Inequalities (LMIs). Furthermore, some slack variables are introduced by using the Finsler lemma in order to give more relaxation for the studied system. Consequently, the proposed approach is proved to have some less conservative results over than some recent works of the literature. Finally, numerical examples are provided to demonstrate the merit and the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This study proposes a new approach of H ∞ deconvolution filtering of 2D system described by Fornasini–Marchesini model and Tchebichef moments. The challenge of this method is to generate an unknown 2D signal by transmission channel. This canal is mobilized by convolution system and deconvolution filter to rebuild the output signal. To resolve this problem, firstly, we use the Tchebichef moments to extract the feature vectors of a medicinal Cannabis sativa plant for generating the input system with the minimum information. Next, we implement the system with the model of Fornasini–Marchesini for convolution and deconvolution. However, the free matrix variables are used to eliminate coupling between Lyapunov matrix and system matrices to obtain sufficient conditions in linear matrix inequality form to ensure the desired stability and performance of the error systems. Experimental results show that the new approach for H ∞ deconvolution filtering of 2D systems described by the Fornasini–Marchesini model and Tchebichef moments achieves good performance than the recent works. [ABSTRACT FROM AUTHOR]

In this work, we propose a new set of H ∞ deconvolution filtering of 2-D color image using feature extraction of local descriptor and Fornasini-Machesini II (FM-II) model. The principal goal is to design 2-D deconvolution filter to reconstruct the noisy color image with the minimal information extracted from local Krawtchouk moment, Moreover, the filtering error system is asymptotically stable and satisfy the H ∞ performance index. the sufficient condition is given to ensure the H ∞ performance of the filtering error system through the Lyapunov theory, and the local Krawtckouk moment to give the feature extraction according to the order defined in advance instead of the global color image. Moreover, the 2-D deconvolution filter is designed to achieve the H ∞ performance index which the filter parameters are determined with certain optimization resolution. Finally, simulation example is provided to demonstrate the usefulness of the proposed design methods. [ABSTRACT FROM AUTHOR]

This paper deals with $$H_{\infty }$$ filtering problem of linear discrete-time uncertain systems with finite frequency input signals. The uncertain parameters are supposed to reside in a polytope. By applying the generalized Kalman-Yakubovich-Popov lemma, polynomially parameter-dependent Lyapunov function and some key matrices to eliminate the product terms between the filter parameters and the Lyapunov matrices, an improved condition is obtained for analyzing the $$H_{\infty }$$ performance of the filtering error system. Then sufficient condition in terms of linear matrix inequality is established for designing filters with a guaranteed $$H_{\infty }$$ filtering performance level. Finally, a numerical examples are used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The problem of $$H_{\infty }$$ filter design for a class of 2D systems is solved here in the presence of intermittent measurements. Data dropouts are characterized using a stochastic variable satisfying the Bernoulli random binary distribution. Our attention is focused on the design of reduced-order $$H_{\infty }$$ filters such that the filtering error 2D stochastic system is robust mean-square asymptotically stable and fulfills a given $$H_{\infty }$$ disturbance attenuation level. We use a new formulation for a class of 2D system Fornasini-Marchesini (FM) models. A sufficient condition is established by means of the linear matrix inequalities (LMI) technique. The efficiency and viability of the proposed techniques and tools are demonstrated through a set of numerical examples. [ABSTRACT FROM AUTHOR]

This paper tackles the $$H_{\infty }$$ filtering problem for 2-D discrete systems. The approach is based on the Roesser model. The objective is to propose a new design with sufficient condition via LMI formulations. Less conservative results are obtained by introducing additional free parameters by using the Finsler's Lemma. This method provides extra degree of freedom in optimization of the $$H_{\infty }$$ performance. The efficiency of the proposed approach is shown by several examples. [ABSTRACT FROM AUTHOR]

This paper investigates the $$H_{\infty }$$ control problem for two-dimensional (2D) Takagi-Sugeno (T-S) fuzzy systems in a Fornasini-Marchesini second (FM II) model with stochastic perturbation. Our aim is to develop the state feedback control for 2D (T-S) fuzzy systems with stochastic perturbation and reduce the conservatism by using some slack matrices. Attention is focused on the design of the state feedback control, which guarantees the closed-loop system to be mean-square asymptotically stable and to have a prescribed $$H_{\infty }$$ performance. Sufficient conditions are derived for the existence of such controls in terms of linear matrix inequalities, and the corresponding control synthesis problem, then, is solved. Numerical examples are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The H filtering problem for two-dimensional Takagi-Sugeno fuzzy systems described by the Fornasini-Marchesini (FM) model is studied. Attention is focused on the design of an H fuzzy filter such that the filter error system is asymptotically stable and preserves a guaranteed H performance. By using basis-dependent Lyapunov functions and adding slack matrix variables, the coupling between the Lyapunov matrix and the system matrices is eliminated. Then, a linear matrix inequality (LMI)-based approach is developed for designing the H fuzzy filter. Finally, an illustrative example is provided to show the effectiveness of the proposed approach and less conservatism. [ABSTRACT FROM AUTHOR]