Biostimulant and Elicitor Responses to Cricket Frass (Acheta domesticus) in Tomato (Solanum lycopersicum L.) under Protected Conditions.

Agriculture in the current century is seeking sustainable tools in order to generate plant production systems with minimal negative environmental impact. In recent years it has been shown that the use of insect frass is an option to be used for this purpose. The present work studied the effect of low doses (0.1, 0.5, and 1.0% w/w) of cricket frass (Acheta domesticus) in the substrate during the cultivation of tomatos under greenhouse conditions. Plant performance and antioxidant enzymatic activities were measured in the study as explicative variables related to plant stress responses in order to determine possible biostimulant or elicitor effects of cricket frass treatments during tomato cultivation under greenhouse conditions. The main findings of this study indicated that tomato plants responded in a dose dependent manner to cricket frass treatments, recalling the hormesis phenomenon. On the one hand, a 0.1% (w/w) cricket frass treatment showed typical biostimulant features, while on the other hand, 0.5 and 1.0% treatments displayed elicitor effects in tomato plants under evaluated conditions in the present study. These results support the possibility that low doses of cricket frass might be used in tomato cultivation (and perhaps in other crops) for biostimulant/elicitor input into sustainable production systems.

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management.

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.