Date of Award
Master of Science (MS)
Dr. A. Bulent Koc
Dr. Joe Mari Maja
Dr. Michael W. Marshall
Dr. Jose Payero
Optimum nitrogen (N) application is essential to the economic and environmental sustainability of cotton production. Variable-rate N fertigation could potentially help farmers optimize N applications, but current overhead irrigation systems normally lack automated site-specific variable-rate fertigation capabilities. The objective of this research was to develop and evaluate an automated variable-rate N fertigation system based on real-time Normalized Difference Vegetation Index (NDVI) measurements from crop sensors integrated with a lateral move irrigation system. For this purpose, NDVI crop sensors and a flow meter were integrated with Arduino microcontrollers and an automated fertigation system was constructed. A computer program was developed to automatically apply site-specific variable N rates based on real-time NDVI sensor data. The system’s ability to use the crop sensor data to prescribe N rates, collect accurate NDVI data from cotton plants on-the-go, the flow meter to monitor the flow of N, apply variable rates of N, and a rotary encoder to establish the laterals position were evaluated using linear regression, ANOVA, two-sample t-tests, and percentage error.
Results from this research showed that the system could accurately use NDVI data to calculate N rates when compared to hand calculated N rates using a two-sample t-test (p>0.05). A two-sample t-test showed that the fertigation system’s sensor data collection was greatly affected by variations of plant height and vigor when compared to a UAV NDVI image (p2 = 0.95) as well as the measured distance travelled using the encoder and the actual distance travelled (slope = 0.9956, R2 = 0.99). Linear regression was used to compare the measured flow rates against the target flow rates for the application accuracy tests. This analysis showed little relationship between to the two populations (slope = 0.1483, R2 of 0.8043).
This research concludes that low-cost Arduino microcontrollers could be integrated with NDVI sensors, flow control valves, flow meters, and rotary encoders to automate N fertigation. Management decisions can be automated using NDVI data from on-the-go handheld GreenSeeker crop sensors, but the crop sensors’ distance from the plant canopy need to be adjusted during application. Additionally, the fertigation system was not able to apply variable rates of N with the design tested. This was due to large pressure fluctuations because the flow of N through the system was not constant throughout application cycles. This research demonstrates how the fertigation system was developed and highlights the aspects which can be improved as well as the current benefits of the system’s design
Bell, Stewart, "Development and Evaluation of an Automated Linear Move Fertigation System for Cotton Using Active Remote Sensing" (2020). All Theses. 3687.