Microtensiometer plant water status sensors for maize irrigation precision and remote sensing ground-truthing

Overview

Accurate control of maize water status is critical for optimum yield. Remote sensing techniques such as NDVI lack the frequency and accuracy needed for precise irrigation management, and conventional soil moisture sensors measure the soil rather than the plant, limiting their reliability. FloraPulse has developed a microtensiometer that directly measures plant water status from inside the plant's water-carrying tissue, offering the highest quality ground-truth data for calibrating remote sensing imagery and enabling more precise irrigation decisions.

Technical specifications

Key features:

• Microtensiometer technology that measures water status directly inside the plant's water-conducting tissue rather than in the surrounding soil
• Embeddable sensor design that enables continuous, real-time monitoring of crop water stress
• Originally developed and validated through year-long trials in tree crops
• Preliminary experiments confirm the sensor can accurately measure water status in potted maize plants
• Designed for installation at representative locations across a maize field to provide ground-truth calibration points for NDVI and related remote sensing imagery
• Supports building correspondence models between NDVI color values and actual plant water stress
• Enables prediction of future NDVI images and interpretation of remote sensing output in terms of plant water stress
• Compatible with cross-validation between multiple sensor installations to evaluate consistency and reliability

Technology readiness level

The microtensiometer has been validated through year-long field trials in tree crops, demonstrating reliable long-term performance. Preliminary experiments have confirmed that the sensor can accurately measure water status in potted maize plants. The next validation step involves installing five FloraPulse systems at representative points in a 100-acre maize field, in partnership with a corn grower, to compare sensor data against existing NDVI imagery, build correspondence models, and cross-validate predictions across sensor locations. This field-scale validation will determine readiness for broader commercial deployment in maize production.