Water Stress Index Detection Using a Low-Cost Infrared Sensor and Excess Green Image Processing.

Area of Science:

• Agricultural Engineering
• Plant Physiology
• Remote Sensing

Background:

• Precision Irrigation (PI) requires accurate crop water status data for optimal water management.
• Infrared (IR) thermometry and Crop Water Stress Index (CWSI) are effective for monitoring plant water stress.
• Conventional IR equipment for CWSI estimation is often prohibitively expensive for widespread adoption.

Purpose of the Study:

• To develop and validate a novel, low-cost system for detecting crop water stress.
• To utilize infrared sensors and image processing to estimate plant temperature and generate thermal maps.
• To assess the feasibility of using TL indices for CWSI estimation in a cost-effective manner.

Main Methods:

• Development of a low-cost system integrating directional infrared sensors with image processing techniques.
• Measurement of plant temperature using low-cost IR sensors with a defined Field of Vision (FoV).
• Generation of Leaf Temperature Maps through plant segmentation in RGB images and IR sensor data analysis.
• Validation of thermal maps against conventional thermal imaging and estimation of CWSI.

Main Results:

• The developed system successfully measures plant temperature and generates thermal maps indicative of water stress.
• Low-cost IR sensors with directional FoV are effective in identifying water stress conditions.
• The estimated CWSI values derived from the low-cost system are consistent with established literature findings.
• Leaf Temperature Maps generated by the system were validated by thermal images.

Conclusions:

• A low-cost system using IR sensors and image processing can effectively detect crop water stress.
• This technology offers a viable alternative to expensive conventional equipment for PI applications.
• The developed method supports site-specific water management by providing accurate crop water status information.