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.

Black-Box Mathematical Model for Net Photosynthesis Estimation and Its Digital IoT Implementation Based on Non-Invasive Techniques: Capsicum annuum L. Study Case.

Photosynthesis is a vital process for the planet. Its estimation involves the measurement of different variables and its processing through a mathematical model. This article presents a black-box mathematical model to estimate the net photosynthesis and its digital implementation. The model uses variables such as: leaf temperature, relative leaf humidity, and incident radiation. The model was elaborated with obtained data from Capsicum annuum L. plants and calibrated using genetic algorithms. The model was validated with Capsicum annuum L. and Capsicum chinense Jacq. plants, achieving average errors of 3% in Capsicum annuum L. and 18.4% in Capsicum chinense Jacq. The error in Capsicum chinense Jacq. was due to the different experimental conditions. According to evaluation, all correlation coefficients (Rho) are greater than 0.98, resulting from the comparison with the LI-COR Li-6800 equipment. The digital implementation consists of an FPGA for data acquisition and processing, as well as a Raspberry Pi for IoT and in situ interfaces; thus, generating a useful net photosynthesis device with non-invasive sensors. This proposal presents an innovative, portable, and low-scale way to estimate the photosynthetic process in vivo, in situ, and in vitro, using non-invasive techniques.