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Real-Time Crop Monitoring & Reporting

Continuous real-time monitoring of crop conditions through the extraction of BIORISTOR-specific indices and physiological variables.

At the end of the trial, the most significant parameters are analyzed and interpreted in a comprehensive final report, including:
- Detailed description of the measurements performed
- Technical analysis of crop performance
- Insights on plant physiological behavior
- Evaluation of the entire production season

If the farmer chooses to share additional data collected through other monitoring systems or methodologies, PlantBit can provide an integrated comparative report, delivering a deeper and more complete agronomic assessment

Decision Support System (DSS) and Web App

Farmers gain daily access to a dedicated Web App, available from any smartphone or laptop, providing a clear and immediate overview of crop health status.

The platform evaluates plant conditions (Stress / No Stress) using advanced machine learning models, including:
- Random Forest algorithms
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM) networks

These models process BIORISTOR data to deliver reliable, data-driven insights into plant physiological status.

The Web App also provides a predictive irrigation advisory tool based on sensor data and AI-driven analysis.
Each day, the system estimates the millimeters of water to be applied the following day, enabling more precise irrigation planning and improved water-use efficiency.

This Package grants agronomists — or trained farmers — full access to all BIORISTOR features within the Web App.
Users can monitor detailed curves and time-series data.

To ensure data integrity and platform security, raw data download is not available within this package.

Plant Breeding

PlantBit supports plant breeding by enabling in vivo monitoring of plant physiological parameters from the earliest developmental stages.

By measuring signals directly within the plant xylem, breeders obtain data on water status and stress responses, overcoming the limitations of indirect phenotyping methods.

This approach enables earlier discrimination among genotypes based on resource-use efficiency and stress tolerance.

Integrating continuous, plant-level measurements into breeding trials enhances selection accuracy and accelerates the development of resilient, high-performing cultivars adapted to a ever-changing environment.

Our Services

Real-Time Crop Monitoring & Reporting

Decision Support System (DSS) and Web App

Plant Breeding

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