This review focuses on the use of unmanned aerial vehicles (UAVs) in precision agriculture, and specifically, in precision viticulture (PV), and is intended to present a bibliometric analysis of their developments in the field. To this aim, a bibliometric analysis of research papers published in the last 15 years is presented based on the Scopus database. The analysis shows that the researchers from the United States, China, Italy and Spain lead the precision agriculture through UAV applications. In terms of employing UAVs in PV, researchers from Italy are fast extending their work followed by Spain and finally the United States. Additionally, the paper provides a comprehensive study on popular journals for academicians to submit their work, accessible funding organizations, popular nations, institutions, and authors conducting research on utilizing UAVs for precision agriculture. Finally, this study emphasizes the necessity of using UAVs in PV as well as future possibilities.

In this paper, we study the control of probabilistic Boolean control networks (PBCNs) by leveraging a model-free reinforcement learning (RL) technique. In particular, we propose a Q-learning (QL) based approach to address the feedback stabilization problem of PBCNs, and we design optimal state feedback controllers such that the PBCN is stabilized at a given equilibrium point. The optimal controllers are designed for both finite-time stability and asymptotic stability of PBCNs. In order to verify the convergence of the proposed QL algorithm, the obtained optimal policy is compared with the optimal solutions of model-based techniques, namely value iteration (VI) and semi-tensor product (STP) methods. Finally, some PBCN models of gene regulatory networks (GRNs) are considered to verify the obtained results.

In this paper, the state-flipped control technique is explored to investigate the stabilization of probabilistic Boolean networks (PBNs). Changing the values of many nodes from 0 to 1 (or from 1 to 0) is called the state-flipped control. The concepts of fixed point, reachable sets, and finite-time global stabilization of PBNs under state-flipped control are proposed. Several necessary and sufficient conditions for global stabilization are also derived based on the reachable sets of a given state. Furthermore, a model-free reinforcement learning (RL) algorithm, namely $Q$ -learning ( $Q$ L), is presented to design a flip sequence for any state that steers the state to a given destination state, thereby achieving finite-time global stabilization via state-flipped control. In addition, the process of finding the minimum flip set is proposed under the semi-tensor product and $Q$ L methods. Finally, the viability of the results in the paper is shown by considering a 12-gene hepatocellular cancer cell tumor network.

In this letter, the output tracking control design of switched Boolean control networks (SBCNs) is investigated via state feedback and output feedback control. The algebraic state-space representation method which resorts to the semi-tensor product (STP) of matrices is utilized; necessary and sufficient conditions for the solvability of the output tracking control problem are presented. A constructive procedure is given to obtain all possible switching signal-dependent state feedback and output feedback controllers under arbitrary switching signals such that the output of SBCNs tracks a time-varying reference trajectory. Finally, a SBCN model of the lactose regulation in the Escherichia Coli bacteria is considered to illustrate the effectiveness of the proposed results.