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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #65507
Title: SENSORS FOR PRECISION AGRICULTURE

Author
item BIRRELL, STUART - UNIV OF MO
item Sudduth, Kenneth - Ken
item Hummel, John

Submitted to: American Society of Agri Engineers Special Meetings and Conferences Papers
Publication Type: Abstract Only
Publication Acceptance Date: 11/4/1995
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: The implementation of site specific crop management (SSCM) depends on accurate field mapping of soil parameters. Electronic, automated sensing of soil properties is important for efficient implementation of SSCM strategies. Our research in non-invasive, optical sensing of soil properties has focussed on developing an instrument to acquire NIR soil reflectance data at a number of narrow-band wavelengths, to measure soil organic matter (SOM) for control of variable rate application. This sensor accurately predicted organic matter content (r**2=0.89, standard error of prediction (SEP)=0.39% SOM) across a range of soil types and moisture contents, under laboratory conditions. NIR estimation of soil moisture was also successful (r**2=0.94, SEP=1.9% moisture). Ion-selective field effect transistors (ISFETs) high signal-to-noise ratio, low sample volumes and ability to simultaneously measure different nutrients make these sensors attractive for real-time soil nutrient sensing. Four nitrate ion-selective membranes were deposited on a multi-ISFET chip integrated into a flow injection analysis system (sampling period=1.25 s). The correlation between predicted and actual soil nitrate concentration for the ISFETs ranged from 0.70 to 0.98, for soil extracts. On claypan soils, crop production is often limited by the thickness of the topsoil above the claypan horizon. Electromagnetic induction (EM) methods of soil conductivity measurement have been used to estimate the topsoil depth. EM measurements across an area with rapid spatial variations in topsoil depth were successfully regressed (r**2=0.81) against the depth detected by soil probing.