Bio-Convergence to Detect Plant Stress

Prof. Adi Avni and Prof. Yosi Shacham-Diamand

School of Plant Sciences and Food Security, Faculty of life sciences and The department of Physical Electronics, school of electrical engineering, faculty of engineering,

Prof. Anvi's and Shacham's research focused on synthetic biology and
genome editing via the CRISPR/Cas9 system and on using plants as biosensors, to detect plant stress.
Recently, they develop biosensors for agriculture where we use micro and nanoscale technologies.
The goal is to develop low-cost sensors that will be integrated into the plant for early detection of various parameters that are the key factors in the food chain (i.e. farmers, wholesalers, transportation, government, and the food industry in general and the customers.

We propose a new paradigm for data-driven precision agriculture, using bio-convergence-based
functional sensors to directly detect plant stress.
The ever-increasing global population, combined with reducing agricultural efficacy due to climate
change poses a significant challenge to meeting the world’s food requirements. Technological
advances, in particular advances in agricultural sensor technology, have been noted to be one of the
most promising directions in addressing this issue. Bio-convergence-based approaches can help
improve the efficacy of agricultural sensing, whilst maintaining sustainability and cost-effectiveness.
Using these approaches, we propose to develop a novel plant-based biosensor that can report the
health and well-being of plants, using direct sensing of plants’ genetic response to stress. This can
allow for in-vivo, reliable, real-time reporting on the needs of plants, at a lower cost than existing
sensor technologies. We propose to execute this using two major research and development
directions: novel bio-synthetic logic gates for efficient detection of multiple stress parameters, and
novel families of electrochemical sensing interfaces with intrinsic amplification. The development of
the proposed sensors will include mathematical/circuit-based modeling, implementation in plants,
and investigations into practicality for on-field use