Function: These sensors are deployed across the pivot to capture a range of data reflected from the crops and soil. By analyzing the spectral data, the sensors can assess various plant health indicators, such as plant stress, and canopy cover.

Data Collected: The multi-spectral sensors provide insights into the plant's health and can identify areas that may require more or less water based on their condition.

Function: These probes are installed at different soil depths to measure the volumetric water content in the soil. They provide real-time data on how much moisture is present at various levels, which helps in understanding the soil's water-holding capacity and how it's being utilized.

Data Collected: Soil moisture readings inform the system about the current state of the soil, indicating whether it is dry, optimal, or saturated.

Function: The AI system processes data from both the multi-spectral sensors and soil moisture probes. It uses advanced algorithms and machine learning models to analyze this data and predict the water needs of different areas of the field.

Data Processing: AI interprets the information to identify patterns and correlations, adjusting its recommendations based on crop type, growth stage and historical data.

Function: Based on the insights provided by the AI, the system can adjust each sprinkler's output individually. This allows for precise control over the amount of water distributed across different areas of the field.

Implementation: Each sprinkler is equipped with a valve that can be adjusted in real-time. The AI system sends commands to these valves to increase, decrease, or maintain water flow as needed to achieve the soil moisture target set by the grower.