Development of an Automated Linear Move Fertigation System for Cotton Using Active Remote Sensing

• Published in: AgriEngineering Vol. 4; no. 1; pp. 320 - 334
• Main Authors: Bell, Stewart, Koc, A. Bulent, Maja, Joe Mari, Payero, Jose, Khalilian, Ahmad, Marshall, Michael
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2022
• Subjects: Arduino; Automation; Coders; Cotton; Crops; Farmers; Fertigation; Fertilizers; Flow rates; Flow velocity; Flowmeters; Irrigation; Irrigation systems; Mathematical analysis; Microcontrollers; Nitrogen; Normalized difference vegetative index; Optimization; Real time; Regression analysis; Remote sensing; Sensors; Shaft encoders; United States > US
• ISSN: 2624-7402; 2624-7402
• DOI: 10.3390/agriengineering4010022

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 study was to develop an automated variable-rate N fertigation 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 integrated with Arduino microcontrollers were constructed on a linear move fertigation system at the Edisto Research and Education Center in Blackville, South Carolina. 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 NDVI data to prescribe N rates, the flow meter to monitor the flow of N, and a rotary encoder to establish the lateral’s position were evaluated. Results from this study 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). Linear regression analysis showed a strong relationship between flow rates measured using the flow meter and hand calculations (R2 = 0.95), as well as the measured distance travelled using the encoder and the actual distance travelled (R2 = 0.99). This study concludes that N management decisions can be automated using NDVI data from on-the-go handheld GreenSeeker crop sensors. The developed system can provide an alternative N application solution for farmers and researchers.