# Method for the detection of powdery mildew in tomato from electrical signalling

## TL;DR

This paper presents a method to detect powdery mildew in tomato plants by analyzing their electrical signals using a Raspberry Pi and custom software.

## Contribution

A novel experimental setup and methodology for detecting plant disease via electrical signal analysis using low-cost hardware and Python programming.

## Key findings

- Electrode material and placement significantly affect the quality of electrical signal measurements.
- Custom Python programs on a Raspberry Pi enable reliable long-term data logging and monitoring.
- Electromagnetic noise immunity is critical for accurate detection of plant electrical signals.

## Abstract

Plants are known to generate various types of electrical signals, which have been observed ever since Darwin’s times. We studied the electrical signals acquired in tomato plants infected with the fungal pathogen Oidium neolycopersici (On), the causative agent of powdery mildew, and applied statistical analyses to detect the differences in electrical responses between healthy and infected plants, as reported in [1].•The underlying mechanism in the generation and transmission of electrical signals is not fully understood, yet it’s generally accepted that they can be classified according to functional properties. Action potentials (APs) and slow wave potentials, in particular, are elicited by biotic and abiotic stimuli, thus are interesting as a hallmark of plant health status.•To analyse the application of these potentials in plant disease detection, voltages from electrodes inserted in plants were acquired periodically by a scanning multimeter and recorded under control of a dedicated custom Python program running on a Raspberry Pi board.•Here we describe the design of the experiment and analyse in some detail the solutions adopted for specific issues found in the measurements, such as electrode’s material and placement; immunity to electromagnetic noise; data logging over long periods of time with intermediate monitoring of results.

The underlying mechanism in the generation and transmission of electrical signals is not fully understood, yet it’s generally accepted that they can be classified according to functional properties. Action potentials (APs) and slow wave potentials, in particular, are elicited by biotic and abiotic stimuli, thus are interesting as a hallmark of plant health status.

To analyse the application of these potentials in plant disease detection, voltages from electrodes inserted in plants were acquired periodically by a scanning multimeter and recorded under control of a dedicated custom Python program running on a Raspberry Pi board.

Here we describe the design of the experiment and analyse in some detail the solutions adopted for specific issues found in the measurements, such as electrode’s material and placement; immunity to electromagnetic noise; data logging over long periods of time with intermediate monitoring of results.

Image, graphical abstract