Non thermal 2.45 GHz electromagnetic exposure causes rapid changes in Arabidopsis thaliana metabolism

Numerous studies report different types of responses following exposure of plants to high frequency electromagnetic fields (HF-EMF). While this phenomenon is related to tissue heating in animals, the situation is much less straightforward in plants where metabolic changes seem to occur without tissue temperature increase. We have set up an exposure system allowing reliable measurements of tissue heating (using a reflectometric probe and thermal imaging) after a long exposure (30 min) to an electromagnetic field of 2.45 GHz transmitted through a horn antenna (about 100 V m^-1 at the plant level). We did not observe any heating of the tissues, but we detected rapid increases (60 min) in the accumulation of transcripts of stress-related genes (TCH1 and ZAT12 transcription factor) or involved in ROS metabolism (RBOHF and APX1). At the same time, the amounts of hydrogen peroxide and dehydroascorbic acid increased while glutathione (reduced and oxidized forms), ascorbic acid, and lipid peroxidation remained stable. Therefore, our results unambiguously show that molecular and biochemical responses occur rapidly (within 60min) in plants after exposure to an electromagnetic field, in absence of tissue heating.

Vision-Based Control in Driving Assistance of Agricultural Vehicles

This article presents a real-time control system for an agricultural mobile machine (vehicle) based on an on-board vision system using a single camera. This system has been designed to help humans in repetitive and difficult tasks in the agricultural domain. The aim of the robotics application concerns the control of the vehicle with regard to the reap limit detected in image space. The perception aspect in relation to the application has been described in previous work, and here we deal with the control aspect. We integrate image features issued from the modeling of the scene in the control loop to perform an image-based servoing technique. The vehicle behavior described here concerns bicycle and neural models, and three control laws are then synthesized. The first and the second are modeling approaches and use an interaction between the scene and the image space. They are based on the regulation of a task function. The third is a "black-box modeling" technique, and is based on a neural network. Finally, experimental results obtained with these different control laws in different conditions are presented and discussed.