MC for Agriculture: A Framework for Nature-inspired Sustainable Pest Control

In agriculture, molecular communication (MC) is envisioned as a framework to address critical challenges such as smart pest control. While conventional approaches mostly rely on synthetic plant protection products, posing high risks for the environment, harnessing plant signaling processes can lead to innovative approaches for nature-inspired sustainable pest control. In this paper, we investigate an approach for sustainable pest control and reveal how the MC paradigm can be employed for analysis and optimization. In particular, we consider a system where herbivore-induced plant volatiles (HIPVs), specifically methyl salicylate (MeSA), is encapsulated into microspheres deployed on deployed on plant leaves. The controlled release of MeSA from the microspheres, acting as transmitters (TXs), supports pest deterrence and antagonist attraction, providing an eco-friendly alternative to synthetic plant protection products. Based on experimental data, we investigate the MeSA release kinetics and obtain an analytical model. To describe the propagation of MeSA in farming environments, we employ a three dimensional (3D) advection-diffusion model, incorporating realistic wind fields which are predominantly affecting particle propagation, and solve it by a finite difference method (FDM). The proposed model is used to investigate the MeSA distribution for different TX arrangements, representing different practical microsphere deployment strategies. Moreover, we introduce the coverage effectiveness index (CEI) as a novel metric to quantify the environmental coverage of MeSA. This analysis offers valuable guidance for the practical development of microspheres and their deployment aimed at enhancing coverage and, consequently, the attraction of antagonistic insects.

ReACT: Reinforcement Learning for Controller Parametrization using B-Spline Geometries

Robust and performant controllers are essential for industrial applications. However, deriving controller parameters for complex and nonlinear systems is challenging and time-consuming. To facilitate automatic controller parametrization, this work presents a novel approach using deep reinforcement learning (DRL) with N-dimensional B-spline geometries (BSGs). We focus on the control of parameter-variant systems, a class of systems with complex behavior which depends on the operating conditions. For this system class, gain-scheduling control structures are widely used in applications across industries due to well-known design principles. Facilitating the expensive controller parametrization task regarding these control structures, we deploy an DRL agent. Based on control system observations, the agent autonomously decides how to adapt the controller parameters. We make the adaptation process more efficient by introducing BSGs to map the controller parameters which may depend on numerous operating conditions. To preprocess time-series data and extract a fixed-length feature vector, we use a long short-term memory (LSTM) neural networks. Furthermore, this work contributes actor regularizations that are relevant to real-world environments which differ from training. Accordingly, we apply dropout layer normalization to the actor and critic networks of the truncated quantile critic (TQC) algorithm. To show our approach's working principle and effectiveness, we train and evaluate the DRL agent on the parametrization task of an industrial control structure with parameter lookup tables.