Listening to Plants with Chips: Decoding Plant Bio Signals

Listening to Plants with Chips Decoding Plant Bio Signals
Smart Farming - AgTech

Dr. Adi Avni

Professor in the school of Plant Sciences and Food Security at Tel Aviv University

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Co-author Prof. Yosi Shacham-Diamand The Scojen Institute for Synthetic Biology, head, Reichman University, Israel. Professor Emeritus, Faculty of Engineering, Tel Aviv University

Listening to Plants with Chips: Decoding Plant Bio Signals with Modern Monitoring and Intervention Technologies

The global population has steadily and rapidly grown over the past century, leading to an increased demand for agricultural produce. A significant factor that limits the supply of agricultural produce is due to losses at all stages: from the field to the market.  A significant loss of essential food crops worldwide, ranging from 12% to 23% of the total produce, occurs before harvest. Addressing this pre-harvest crop loss is a key challenge for increasing agricultural supply under the constraints of diminishing resources. e.g. land and water, and increased regulations regarding the use of chemicals. In recent years, a rising focus has been on data-driven precision agriculture. The challenge in adopting precision agricultural techniques is due to the lack of accurate, real-time, and actionable information on crop health. Collecting this information requires sensors, including transducers and communication technologies, both hardware and software, i.e. interfacing, signal processing, data storage, communication, etc. The key is the sensor technology that provides essential data for the IT systems and the algorithms and tools for optimizing food production from farming to distribution.

Implementing cost-effective sensors in the field enables real-time control over farm resources. This approach is vital in improving growth efficiency and preparing for future seasons. To enhance sensor effectiveness and economic benefits, it is essential to design sensors tailored to meet the needs of farmers during cultivation and subsequent stages like harvesting, storage, and transportation.

Innovative technologies for monitoring plant health 

Cutting-edge technologies for monitoring plant health in agriculture include various imaging solutions such as:

  • multispectral imaging,
  • hyperspectral imaging,
  • thermal imaging, and
  • LiDAR (Light Detection and Ranging)

These technologies enable high-resolution data collection for monitoring crop health, disease detection, nutrient assessment, and identification. Advancements in sensor technology have made these imaging solutions more accessible, affordable, and easier to integrate with existing agricultural machinery and management systems.

Furthermore, integrating artificial intelligence (AI) and machine learning algorithms with imaging sensors allows for the interpretation of vast amounts of data collected, providing actionable insights for crop management.

Drones equipped with imaging sensors are also increasingly used to rapidly monitor large agricultural fields, precisely map crops, assess plant health, and identify areas requiring intervention like irrigation or pesticide application.

Monitoring plant health has become increasingly sophisticated with the advancement of technology. Here’s an overview of some cutting-edge technologies utilized for this purpose:

  1. Remote Sensing: Technologies like satellite imagery, aerial drones, and unmanned aerial vehicles (UAVs) equipped with multispectral or hyperspectral cameras can capture detailed images of crops at different wavelengths. This data can be analyzed to detect stress factors such as nutrient deficiencies, diseases, and pest infestations.
  2. Machine Learning and AI: These technologies are being employed to analyze large datasets generated by remote sensing and other monitoring techniques. Machine learning algorithms can identify patterns and anomalies in plant health data, enabling early detection of diseases or environmental stressors.
  3. Hyperspectral Imaging: This technology enables the capture of detailed spectral information beyond the visible spectrum. Hyperspectral imaging can accurately diagnose plant diseases and nutrient deficiencies by analyzing the unique spectral signatures of healthy and diseased plants.
  4. Internet of Things (IoT) Sensors: IoT sensors can be deployed in fields to monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels in real-time. These sensors provide valuable data for optimizing irrigation schedules, fertilization practices, and detecting environmental stressors.
  5. Nanotechnology: Nanosensors and nanomaterials are being developed for plant health monitoring applications. These nanodevices can detect minute changes in plant physiology and biochemical processes, providing early warning signs of stress or disease.

By integrating these cutting-edge technologies, farmers and researchers can gain valuable insights into plant health dynamics, optimize resource management practices, and enhance agricultural productivity while minimizing environmental impact.

Living sensor plants

We may be unable to talk to plants, but what if they could tell us how they feel anyway?

Future advancements in sensors for precision agriculture will rely on the ability to interact directly with plants about their well-being. This will be achieved by integrating sensors on plants instead of their near environment.

Electrochemical biosensing is particularly interesting in sensing for precision agriculture due to its simplistic nature and ability to be used even under strong sunlight that usually masks optical signals.

Sensors were developed by genetically engineered living sensor plants. These plants are designed to signal when they are stressed due to factors like fungal pressure, pests, nutrient deficiencies, or drought. The leaves of these plants will make the expression of GUS enzyme in response to a specific stress, enabling it to become a stress sensor.

The enzymatic activity of GUS enzyme on β-glucuronidase substrate generates electroactive products that can be electrochemically detected. Like the electrochemical sensors, InnerPlant company developed genetically engineered living sensor plants. The leaves of these plants fluoresce in different colors, providing early warning signals for growers. The technology involves recoding plant DNA with fluorescent proteins that change colors when the plant is stressed or needs water. This technology allows the detection of plant stress within hours of emergence, enabling quick corrective actions to protect crops. One of the key features of this technology is its ability to integrate plant signals with other data sources using machine learning and proprietary software. This integration provides farmers with prescriptive and targeted recommendations for optimizing crop production. This technology serves as a precision early warning system for identifying various plant stresses, such as fungal diseases, insects, nutrient deficiencies, and water stress. Overall, this technology leverages living sensor plants to provide farmers with actionable data, reduce chemical use, increase crop yields, and promote sustainable farming practices in a scalable and affordable manner.

Electrochemical impedance spectroscopy (EIS) is a powerful technique for analyzing the properties of interfaces, particularly those linked to bio-recognition events at electrode surfaces. These events can involve various biological interactions, such as antibody-antigen recognition and substrate-enzyme interaction. Environmental conditions, particularly light and temperature, induce changes in the impedance modulus. Daily impedance changes are correlated with the daily (normal) hydration/dehydration sequence.

Living sensor plants

Four-point impedance spectroscopy can be used for real-time monitoring of plant hydration levels by measuring impedance changes in plant tissues during hydration and dehydration cycles. This method offers a non-invasive and efficient way to assess plant water status under different conditions, providing valuable insights into plant responses to water stress.

On the technological front, innovative tools and methods are being developed to monitor and interact with plants in unprecedented ways. One notable example is the development of visual microphones that can recreate audio from silent video recordings by analyzing subtle movements in objects, including plants. This technology, developed by MIT, Microsoft, and Adobe researchers, demonstrates the potential to decode vibrations in plants caused by sound, offering a novel way to eavesdrop on conversations or recover sounds from surveillance footage.

Another significant advancement is using Internet of Things (IoT) technologies in smart agriculture. These technologies enable the monitoring and control of agricultural processes, such as irrigation and fertilizer management, through sensors and automated systems.

Farmers can optimize water usage, nutrient delivery, and overall crop health by collecting and analyzing data from the environment and plants themselves, leading to more efficient and sustainable agricultural practices.


The decoding of plant biosignals and the application of modern monitoring and intervention technologies offer exciting possibilities for advancing our understanding of plant biology, enhancing agricultural practices, and exploring new forms of interaction with the natural world.


  1. Dotan, T., Jog, A., Kadan-Jamal, K., Avni, A., and Shacham-Diamand, Y. (2023). In Vivo Plant Bio-Electrochemical Sensor Using Redox Cycling. Biosensors (Basel) 13.
  2. Kadan-Jamal, K., Jog, A., Sophocleous, M., Dotan, T., Frumin, P., Kuperberg Goshen, T., Schuster, S., Avni, A., and Shacham-Diamand, Y. (2024). Sensing of gene expression in live cells using electrical impedance spectroscopy and DNA-functionalized gold nanoparticles. Biosens Bioelectron 252, 116041.
  3. Kadan-Jamal, K., Jog, A., Sophocleous, M., Georgiou, J., Avni, A., and Shacham-Diamand, Y. (2021). Towards optimization of plant cell detection in suspensions using impedance-based analyses and the unified equivalent circuit model. Sci Rep 11, 19310.
  4. Pandey, R., Teig-Sussholz, O., Schuster, S., Avni, A., and Shacham-Diamand, Y. (2018). Integrated electrochemical Chip-on-Plant functional sensor for monitoring gene expression under stress. Biosens Bioelectron 117, 493-500.
  5. Urso, M., Tumino, S., Bruno, E., Bordonaro, S., Marletta, D., Loria, G.R., Avni, A., Shacham-Diamand, Y., Priolo, F., and Mirabella, S. (2020). Ultrasensitive Electrochemical Impedance Detection of Mycoplasma agalactiae DNA by Low-Cost and Disposable Au-Decorated NiO Nanowall Electrodes. ACS Appl Mater Interfaces 12, 50143-50151.

Further reading

Innovations & Technologies for Smarter Integrated Pest Management


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