YIELD Test – Yield Improvement using ELectrophysiology Device

Alteration of the environmental conditions are inducing changes in the underlying physiological process of a plant, which are portrayed by distinct variations in the electrical potential that could further be monitored. In previous research work done for Innosuisse-founded project PISA - Platform applying Intelligent Signal Analysis to gain insights to plant electrophysiology, the HEIG-VD team have demonstrated that, by employing advanced signal processing and data analysis techniques, it is possible to recognize patterns in the electrical response of tomato plants growing in typical production conditions, allowing to identify, with accuracy, the presence of stress in plants caused by the applied stressor, such as drought, deficit of nutrients and insect attack.

Early detection of stress in plants would lead to a more effective crop protection and, as consequence, a significant decrease of crop losses due to disease and blights and an increase in production. Moreover, it would further allow a reduced and more effective use of pesticides that would lower the environmental impacts from the agriculture.

By extending the knowledge of previous related work, the main objective of this project is to provide new insights on plant electrophysiology that could bring direct benefit to growers in terms of optimizing the production conditions and yields. More precisely, within a multi-disciplinary collaboration, the HEIG-VD team will aim at answering three research questions:

  1. Can the model built for diagnosing stress in tomatoes be applied to other crops?
  2. Can a specific model combining crop electrical response to drought, nutrient deficiencies and insect attacks be created to provide a generalized measure of plant stress?
  3. Can electrophysiology measurements be used to enhance scouting in commercial greenhouses?

Project funded by the swiss Federal Office for Agriculture - FOAG.

Publications:

  • Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology Najdenovska, Elena; Dutoit, Fabien; Tran, Daniel; Rochat, Antoine; Vu Basile; Mazza Marco; Camps, Cédric; Plummer, Carole; Wallbridge, Nigel; Raileanu, Laura, Applied Sciences 2021, vol. 11, no. 12, article no. 5640 https://doi.org/10.3390/app11125640