Calibration and algorithm development for estimation of nitrogen in wheat crop using tractor mounted N-sensor.

Area of Science:

• Agronomy
• Precision Agriculture
• Remote Sensing

Background:

• Optimizing nitrogen (N) application is crucial for wheat yield and environmental sustainability.
• Tractor-mounted sensors offer potential for real-time crop nutrient monitoring.

Purpose of the Study:

• To evaluate a tractor-mounted N-sensor (Yara International) for predicting nitrogen requirements in wheat.
• To develop and validate algorithms for N application based on sensor data.

Main Methods:

• Spectrometers on the N-sensor scanned approximately 32% of the crop area.
• A linear algorithm was developed correlating sensor sufficiency index (SIsensor) with SISPAD for N application prediction.
• Sensor attributes (value, biomass, NDVI) were analyzed against different N levels.

Main Results:

• Strong correlations were found between sensor attributes and various N levels.
• The tillering stage showed the highest prominence for predicting crop yield using sensor data.
• Developed algorithms for tillering and booting stages accurately predicted N application rates.

Conclusions:

• Tractor-mounted N-sensors can effectively predict nitrogen needs for wheat crops.
• The tillering stage is the most effective for yield prediction using sensor attributes.
• Algorithms developed enable precise N application, matching predicted rates with actual sensor values.