Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

The blades in a wind turbine are currently manufactured with flexible and light materials, which make them more susceptible to the effects of vibrations when the wind speed is high enough, causing fatigue damage, affecting the functionality of its structure and aerodynamic efficiency. This work presents a comparison of the modal vibration parameters, applied to a cantilever beam, determined with two experimental methods—the use of accelerometers and a proposed optical non-contact method—based on the principle of laser triangulation and photogrammetry techniques. This technique uses the geometric symmetry of the equidistant displacements along the z axis of the beam to obtain the amplitude data. Parameters such as natural frequency and modal form are obtained by fitting the data to a nonlinear equation with a solution which is an exponential/harmonic equation. Also, analytically, these parameters are determined, and a comparison is made between the experimental methods. The result shows that the relative error of the first-order natural vibration frequency is below 1%. The proposed method is simple, efficient, reliable, and it is also a method that has not been applied to the test of wind turbine blades, so its implementation as this type of wind turbine component is an area of opportunity for the validation of modal vibration parameters in the wind industry. An analysis of results is presented showing benefits of the proposed method and its limitations.

This article presents the analysis of the performance of a flexible wind turbine blade. The simulation analysis is based on a 3 m span blade prototype. The blade has a flexible surface and a cam mechanism that modifies the aerodynamic profile and adapts the surface to different configurations. The blade surface was built with a flexible fiberglass composite, and the internal mechanism consists of a flexible structure actuated with an eccentric cam. The cam mechanism deforms five sections of the blade, and the airfoil geometry for each section was measured from zero cam displacement to full cam displacement. The measured data were interpolated to obtain the aerodynamic profiles of the five sections to model the flexible blade in the simulation process. The simulation analysis consisted of determining the different aerodynamic coefficients for different deformed surfaces and a range of wind speeds. The aerodynamic coefficients were calculated with the BEM method (QBlade^®); as a result, the data performance of the flexible blade was compared for the different deformation configurations. Finally, a decrease of up to approximately 6% in the mean bending moment suggests that the flexible turbine rotor presented in this article can be used to reduce extreme and fatigue loads on wind turbines.