The agricultural sector is one of the socio-economic systems most exposed to the impacts of climate change. At the same time, agriculture faces a historic challenge: feeding a rapidly growing global population while preserving the planet’s finite natural resources.
Producing more with less is no longer optional. It requires a profound transformation of agricultural practices.
Technology is a key enabler of this transformation, playing a central role in improving the sustainability of agricultural production and contributing directly to the achievement of UN Sustainable Development Goals 2 (Zero Hunger) and 13 (Climate Action).
One of the core objectives of Agriculture 4.0 is the optimization of water use. Today, agriculture accounts for approximately 70% of global freshwater consumption, making water efficiency one of the most critical challenges for the sector.
Our work focuses on the development and application of an innovative plant-based biosensor — an Organic Electrochemical Transistor (OECT) — designed to be inserted directly into the stem of the plant.
This sensor enables continuous, real-time monitoring of variations in the ionic composition of plant sap, providing direct insight into the physiological state of the plant.
The sensor, called BIORISTOR, consists of a transistor channel and a secondary electrode. When an appropriate electrical potential is applied, an electric field is generated that drives ions present in the sap into the conductive polymer deposited on the main channel.
This mechanism allows the BIORISTOR to detect subtle and early physiological changes inside the plant.
The BIORISTOR has been extensively tested under controlled conditions on a wide range of species, including tomato, kiwi, soybean, grapevine, wheat, and river reed.
In tomato plants, the system is capable of detecting the onset of water stress within the first 30 hours, well before visible symptoms appear.
In field applications, the BIORISTOR is connected to an IoT control unit that transmits data to a dedicated application.
The system detects the early onset of water stress and can alert the farmer or farm management systems when irrigation is needed, enabling timely, targeted interventions. This approach significantly reduces water waste while improving crop performance and sustainability.
The BIORISTOR represents a new paradigm in precision agriculture, providing direct, plant-level information on real physiological conditions and actual needs — rather than relying solely on indirect environmental measurements.
The data collected by the sensor is continuously processed using advanced analytical models and artificial intelligence algorithms, including:
Random Forest models to identify the current health status of plants
Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) to predict plant responses and stress conditions up to 24 hours in advance
Rather than offering automated prescriptions for fertilization or phytosanitary treatments, the BIORISTOR platform is complemented by a dedicated agronomic consultancy service.
Experienced specialists further analyze sensor data, integrating it with agronomic knowledge to support farmers and agronomists in:
- Interpreting plant responses
- Making informed decisions on irrigation strategies
- Better understanding crop behavior and stress dynamics
- Optimizing interventions based on real plant needs
This human-in-the-loop approach ensures reliability, adaptability, and trust, especially in complex and variable field conditions.
All raw data is securely stored in a structured SQL database, organized into dedicated thematic tables. This enables customers to develop data-driven production strategies that improve crop quality and yield, increase operational efficiency, reduce waste, and enhance overall farm profitability.
Field trials indicate that the use of the BIORISTOR can reduce irrigation water consumption by up to 40%.
Thanks to its unique timeliness and precision, the system enables interventions exactly when and where they are needed, helping to prevent stress-related yield losses and improve the long-term sustainability of agricultural production.
