Agricultural Machinery Conference May, 006 On-the the-go Soil Sensing Technology Viacheslav I. Adamchuk iological Systems Engineering University of Nebraska - Lincoln Agenda Family of on-the-go soil sensors Nebraska examples Deep tillage implement Integrated mapping of soil physical properties Soil mechanical resistance Dielectric characteristics (moisture) Subsurface optical reflectance Integrated agitation chamber module Soil ph Soluble potassium content Residual nitrate content Portable probe for on-the-spot measurements Sensor fusion On-the the-go Soil Electrical and Electromagnetic Electrical Conductivity/Resistivity Electrical and Electromagnetic Optical and Radiometric Electromagnetic Induction Method Galvanic Contact Resistivity Method Capacitively-Coupled Resistivity Method Capacitance Acoustic Mechanical Electrochemical H + H + H + H + Pneumatic Soil type/texture Salinity Organic matter content Depth variability Soil ph / nitrate content EC EC Volumetric water content Soil type/structure Salinity EC H + EC EC 4 EC 3 Soil Soil Optical and Radiometric Subsurface Soil Reflectance Subsurface Soil Reflectance Visual Near-infrared Mid-infrared Image analysis Polarized light Organic matter (carbon) content Soil texture Cation exchange capacity (CEC) Soil water content Soil ph Mineral nitrogen and phosphorous Microwave Ground Penetrating Radar Geophysical soil structure Shank DGPS Antenna Notebook Computer Coulter Spectroradiometer Individual Wavelengths 660 nm LEDs Photodiode Purdue University (West Lafayette, Indiana) Hyperspectral Response Light Source Shank Reflectance, %.00 0.9 0.90 0.8 0.80 0.7 0.70 0.6 0.60 0. 0.0 Nebraska soils 900 000 00 00 300 400 00 600 700 Wavelength, nm
Real-time Soil Mapping with a Traveling Spectrophotometer Optical fibers for illumination Laser displace sensor Ground surface Shank Optical fiber for visible reflection CCD camera Optical fiber for NIR reflection NIR thermometer Single Estimate Implement Draft Mechanical Strain Gage Transducers Profile Assessment Load Cells Penetrator tip Soil flattener Travel direction Load Cell EC Electrode Soil surface illumination Tokyo University of Agriculture and Technology (Tokyo, Japan) Tool ar Strain gages Travel direction Soil mechanical resistance Soil compaction Soil types Depth of hard (plow) pan Soil-metal friction Discrete Depth Profiling Tools Load Cell Array Three Cutting lades Example Soil Mechanical Resistance Map Compacted area Old roads UC-Davis (Davis, California) University of Missouri (Columbia, Missouri) Soil Mechanical Resistance Map (0-30 cm) Yield Map Instrumented Deep-Tillage Implement Vertical lade with Strain Gage Array Discrete Model Polynomial Model Linear Model Strain gages Strain Gages Soil Surface Soil Strength Soil surface Apparent soil surface Load Cells Depth Travel direction
3 9 7 3 3 9 7 3 Apparent Soil Profiles Integrated Load Comparison Relative depth, cm Soil mechanical resistance, MPa -0.8-0.4 0.0 0.4 0.8..6.0.4.8 0 0 ISPPMS - pass Cone - pass 0 ISPPMS - pass Cone - pass Plot (disked) Relative depth, cm Soil mechanical resistance, MPa -0.8-0.4 0.0 0.4 0.8..6.0.4.8 0 ISPPMS - pass Cone - pass ISPPMS - pass Cone - pass 0 0 Plot D (chiselled and disked) Tillage plot A C D E F Instrumented lade pass Center row Wheel row 0 000 4000 6000 8000 0000 Overall integrated load (ISPPMS), N Tillage plot A C D E F Cone Penetrometer pass Center row Wheel row 0 000 4000 6000 8000 0000 Overall integrated load (cone penetrometer), N 8 a.68903 *0 6 a = 6.789*0 4 a0 6.6388 *0 8 7.368 *0.79098 *0 4.30 *0 p ( y) = a y + a y + a0 8 4.4800 *0 RA RA 8.70 6 6.67377 *0 R R =.77 4.600 *0 R C RC 0.040 9.008.969 0.044 3.0 37.87.0893 ε.99 ε 39.04 ε 3.84 ε 4 Tillage Plot A C Treatment plowed and double disked disked no-till with cultivation Tillage Plot D E F Treatment chiselled and disked double disked no-till w/o cultivation Integrated Soil Physical Properties Mapping System Integrated Soil Physical Properties Mapping Two wavelengths soil reflectance sensor Soil mechanical resistance profiler with an array of strain gage bridges Capacitor-based sensor Acoustic and Pneumatic Electrochemical Soil Penetration Noise Air Permeability Sensor Ion-Selective Electrodes (ISEs) Ion-Selective Field Effect Transistors (ISFETs) Soil Solution Measurement New Automated Methods Direct Soil Measurement University of Illinois (Urbana-Champaign, Illinois) Soil clay content (type) Soil compaction Depth of hard (plow) pan Soil structure/tilth Soil type University of Kentucky (Lexington, Kentucky) Conventional Laboratory Analysis Activity of selected ions Soil ph (H + ) Potassium content (K + ) Residual nitrogen (NO 3- -N) Sodium content (Na + ) 3
Automated Soil Solution Measurement Mobil Sensor Platform (MSP) Soil cutters Coring tube Mixing ISFET Electrode ~0 g soil core Water jet EC Surveyor 30 Add 0 ml DI H O Cleaning Sample collection for calibration Purdue University (West Lafayette, Indiana) JTI (Uppsala, Sweden) Veris Technologies, Inc. (Salina, Kansas) http://www.veristech.com Soil ph Manager Automated Direct Soil Measurement Purdue University (West Lafayette, Indiana) Veris Technologies, Inc. (Salina, Kansas) Ion-selective Electrodes Water Nozzle Directed Soil Sampling Example Soil ph Maps Soil ph Maps of a 4-ha Kansas Field Soil Sampler On-the-Go Mapping Conventional ha Grid Sampling Integrated Direct Soil Measurement Integrated Agitated Soil Measurement 7. 6. R = 0.93 (0.96 means) RMSE (Precision) = 0. ph Reg. SE (Accuracy) = 0.6 ph Motor-Stirrer Nebraska soils with fixed field water content Measured ph..0 4. ph 4..0. 6. 7. Ion-selective Electrodes (ISE) Reference ph Measured soluble potassium, mg/kg 00 0 pk R = 0. (0.6 means) RMSE (Precision) = 0.3 pk Reg. SE (Accuracy) = 0. pk 0 00 000 Reference soluble potassium (AAS), mg/kg Measured nitrate-nitrogen, mg/kg 000 R = 0.3 (0.6 means) RMSE (Precision) = 0.9 pno3 Reg. SE (Accuracy) = 0. pno3 00 0 pno 3 0 00 Reference nitrate-nitrogen (CR), mg/kg Agitation Chamber and Stirrer Soil Sampler 4
Integrated Agitated Soil Measurement : solutions with Nebraska soils Measured ph 7. 6...0 4. ph R = 0.87 (0.9 means) RMSE (Precision) = 0. ph Reg. SE (Accuracy) = 0.0 ph 4..0. 6. 7. Reference ph ASM - Laboratory Test Reference tests: Soil ph - glass ion-selective electrode - RMSE = 0.0 ph Soluble potassium - atomic adsorption spectroscopy (AAS) - RMSE = 0.0 pk - Residual nitrate - cadmium reduction (CR) - RMSE = 0.0 pno 3 Measured ph 6...0 4. R = 0.98 (0.99 means) RMSE (Precision) = 0.08 ph Reg. SE (Accuracy) = 0.09 ph ph 4..0. 6. Reference ph 00 00 000 00 Measured soluble potassium, mg/kg pk 0 R = 0.4 (0.63 means) RMSE (Precision) = 0.0 pk Reg. SE (Accuracy) = 0.3 pk 0 00 000 Measured nitrate-nitrogen, mg/kg R = 0.3 (0.40 means) RMSE (Precision) = 0.7 pno3 Reg. SE (Accuracy) = 0. pno3 0 pno 3 0 00 Measured soluble potassium, mg/kg R = 0.9 (0.98 means) RMSE (Precision) = 0.0 pk Reg. SE (Accuracy) = 0.03 pk 00 0 pk 0 00 000 Measured nitrate-nitrogen, mg/kg pno 3 0 R = 0.48 (0.67 means) RMSE (Precision) = 0.3 pno3 Reg. SE (Accuracy) = 0.0 pno3 0 00 Reference soluble potassium (AAS), mg/kg Reference nitrate-nitrogen (CR), mg/kg Reference soluble potassium (AAS), mg/kg Reference nitrate-nitrogen (CR), mg/kg Precision vs. Accuracy Analysis Applicability of On-the the-go Soil Accuracy error, px 0.30 0. 0.0 0. 0.0 0.0 Poor performance Acceptable performance 0.00 0.00 0.0 0.0 0. 0.0 0. 0.30 Precision error, px ph - Lab ASM pk - Lab ASM pno3 - Lab ASM ph - ASM pk - ASM pno3 - ASM ph - DSM pk - DSM pno3 - DSM ph - Lab (ISE) pk - Lab (AAS) pno3 - Lab (CR) Acceptance Soil property H + H + H + H + H + Soil texture (clay, silt and sand) Good OK Soil organic matter or total carbon Good Soil water (moisture) Good Good Soil salinity (sodium) OK Soil compaction (bulk density) Good Depth variability (hard pan) OK Soil ph Good Residual nitrate (total nitrogen) OK Other nutrients (potassium) OK CEC (other buffer indicators) OK OK Portable Probe On-the the-spot Measurement of Soil ph Soil ph 8.0 7. 6...0 4. 3. Portable Probe Plot A Plot Plot C Average Plot A Average Plot Average Plot C Reference Lab 3.0 0 0 0 30 40 0 ~ 8 ~ 8 ~8 Depth, cm 0 0
Summary On-the-go soil sensors can provide high density information about soil properties Our ability to map specific agronomic soil attributes remains questionable Combining (fusion) different sensors may be beneficial New and improved sensors are under development Agro-economic evaluation of the value of information is needed http://bse.unl.edu/adamchuk E:mail: vadamchuk@.unl.edu 6