REAL-TIME VARIABLE RATE TECHNOLOGIES (VRT) Frédéric René-Laforest Master Candidate Bioresource Engineering Macdonald Campus of McGill OUTLINE VRT Concept and Architecture VRT Applications and Results Liquid Manure Herbicide Forages Harvesting (Mowing) Planting 2 PRECISION AGRICULTURE GLOBAL NAVIGATION SATELLITE SYSTEM BASED MACHINERY GUIDANCE to optimize crop production and reduce the environmental footprint by using new digital technologies. - Gebbers, R. and V.I. Adamchuk 3 4 VARIABLE RATE TECHNOLOGIES Adoption limited? Lack of robust decision making principles High cost = Research TECHNOLOGY ARCHITECTURE Internal Sensor External Sensor Prescription Map Relation? Tractor Controller Algorithm Implement 5 6
Sensor Input (Elevation) Control Output (Speed) MANURE APPLICATION Sa Sb Sc Stage Ea Control Output (Speed) Smax Sa Sb Smin Eb Proportional Prescription Map Based HERBICIDE MANAGEMENT & MOWING CONDITIONING Crop Reflectance Sensor Low Green Reflectance Ultrasonic Sensor High Biomass Height Ea Eb Sensor Input (Elevation) 7 High Green Reflectanc e Low Biomass Height 8 VARIABLE DEPTH PLANTING Moisture Sensor 9 Data Acquisition System (DAQ) CPU & Software Electronic Controller Adjustable Seeder's Depth Wheels Actuator 10 MOISTURE SENSOR Moisture Sensor Detail A Scale 2 : 15 Electronics Actuators Depth Control Leverage 11 12
2013 VARIABLE DEPTH PLANTING 2014 VARIABLE DEPTH PLANTING Relatively Moist Area Relatively Dry Area Shallow-Dry D Deep-Dry C Deep-Moist A Shallow-Moist B 96 rows 48 rows 13 14 FIELD CONDITIONS RESULTS June 25, 2014 11:00 am Field 24, Ste-Anne-de-Bellevue, Québec 15 16 MANURE APPLICATION 20 to 30% environmentally safe additional manure discharge Stage Proportional HERBICIDE & MOWING Tractor speed controlled precisely Real-Time response 2.5 High vegetation Low vegetation Speed (1.5s offset) 120 2.0 Vegetation height 100 Travel speed, m/s 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Low elevation Medium elevation High elevation Travel speed, m/s 80 1.5 60 1.0 40 0.5 20 0.0 0 0 10 20 30 40 50 60 70 80 90 Time, s Vegetation Height, cm 0 1000 2000 3000 4000 5000 Time, s 17 18
PLANTING: EMERGENCE Depth 7.5 cm Depth VARIABLE DEPTH PLANTING 2013 EXPERIMENT 19 Relatively Wet Area 20 PLANTING: ROOTING SYSTEM 7.5 cm 7.5 cm RESULTS: ROOTING SYSTEM (A) RWA (C) RDA 2.5 cm 2.5 cm Depth 7.5 cm Depth (B) RWA (D) RDA 21 Relatively Dry Area 22 RESULTS: COBS RESULTS: YIELD Depth 7.5 cm Depth 16 28 cobs 27 cobs Yield Estimate (Mg/ha) 15 14 13 12 11 2.5 cm 7.5 cm 10 A B C D RWA RDA Seeding Conditions Relatively Dry Area 23 24
PRELIMINARY RESULTS 2.5 cm VARIABLE DEPTH PLANTING 2014 EXPERIMENT 7.5 cm 25 Relatively Dry Area 26 POST EMERGENCE UAV PICTURES 2 in 3 in 1 in 2 in 3 in 1 in 2 in 3 in 1 in 2 in CONCLUSION GNSS (GPS) Prescription Map Crop Reflectance Moisture Sensor Ultrasonic Sensor. Robust Decision Making Controller (Algorithm) 27 28 QUESTIONS? 29
Background and Objective Mapping soil water content using an on-the-go capacitive moisture sensor Maria Mastorakos, Viacheslav I. Adamchuk, Frédéric René-Laforest Department of Bioresource Engineering, McGill University Macdonald Campus Ste-Anne-de-Bellevue, Quebec Charles R. Hempleman Retrokool Inc., Berkeley, California Monitoring agricultural field soil moisture has many useful applications. Currently, accurate moisture sensors are point based and cannot provide true picture of field s moisture heterogeneity. Possible uses of an on-the go moisture sensor: variable depth planting, after-harvest soil water content mapping. Objective was to build capacitance based sensor that is capable of collection soil moisture data in real-time. Sensor Design Sensor Design Two (2) stainless steel electrodes connected to proprietary electronic circuit (Retrokool, Inc) via coaxial cable. Teflon casing. Electrodes cut at angle for proper contact with soil. Lab Calibration Lab Calibration 5 soils tested Clay loam, sandy clay loam (2 samples), loam, sandy loam. Soil moisture was measured via sensor and ovendrying method. Sensor was suspended in air before being depressed for each replication to measure voltage drop (Vair to Vsoil). Voltage drop matched to oven-dried moisture level. Gravimetric Soil Water Content (g/g) Gravimetric Water Content Volumetric Water Content 0.6 R2 = 0.61 SE = 0.078 g/g 0.5 0.4 0.3 0.2 Clay Loam Sandy Clay Loam 1 0.1 Sandy Clay Loam 2 Loam Sandy Loam 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Soil Water Sensor Index Volumetric Soil Water Content (%) 60 R 2 = 0.61 SE = 7.7% 50 40 30 20 Clay Loam Sandy Clay Loam 1 10 Sandy Clay Loam 2 Loam Sandy Loam 0 0.5 1.0 1.5 2.0 2.5 3.0 Soil Water Sensor Index w 0.61 0.21log( V air Vsoil ).4 22.3log( V V ) 65 air soil 1
Field Testing Field Testing Sensor was attached to planter via coulter disc and pulled behind tractor during seeding. Point-based measurements using time domain reflectometer (TDR) probe were taken after sensor had been pulled through soil. Voltage drop matched to TDR measurements using GPS points. Planter Moisture Sensor Future Plans Improve robustness of sensor. More laboratory calibrations to examine more closely effect of soil type (i.e. clay) on results and determine if different soil types require different calibration equations. More field tests. Email: maria.mastorakos@mail.mcgill.ca 2
2014 ASABE/CSBE Annual International Meeting Montréal, Québec Mapping forage crop biomass with an integrated proximal sensing system Yue Su Dr. Viacheslav Adamchuk Frédéric ric René-Laforest Department of Bioresource Engineering McGill University Gustavo Portz University of São Paulo Background Site specific crop management of agricultural fields responds to localized plant needs with optimized amount of chemical and fertilizer application increase in farming efficiency reduces the negative environmental impact Yield mapping has become a popular practice for grain crops, forage crop production lags behind in term of collecting yield-related information October 24, 2014 Objective Creating an on-the-go integrated proximal sensing system for mapping forage crop biomass Spectrum: Holland Scientific Crop Circle - ACS-430 Sensors Spectral sensors Ultrasonic Proximity Sensors IR Temperature Sensors GPS Measured output Red-Edge (RE) Red Near Infrared (NIR) Normalized Difference VI (NDVI) Normalized Difference Red- Edge (NDRE) Wavelength / Formula 670 nm 730 nm 780 nm Sensors IR Thermometer Process Sensors Co. PCS-SSS Ultrasonic Proximity Sensor (UPS) Senix Co. TSPC-30S1 Operational distance 10.2cm to 2m Global Positioning System (GPS) Garmin Model 19x 2013 SENSORS CALIBRATION August 9 th, 2013
Forage Yield Calibration Process Section B Garmin GPS x19 Spectrum Sensor (CropCircle ACS-430) Section A 50cm 50cm Experimental design Sensor Samples Rep Left side Right Side Ultrasonic 20 2 YES YES Proximity Crop Circle 20 1 YES NO Compensate Crop Circle 20 1 NO YES Non- Compensate IR Thermometer 20 1 Middle Before harvest IR Thermometer (Process Corp. PCS-SSS) Ultrasonic Proximity (Senix TSPC-30S1) After harvest Calibration Results Calibration Results Mechanical Design New Holland 2008 Discbine 1431 Disc Mower Conditioner 2013 - INTEGRATED SYSTEM FOR YIELD MONITORING August 15 th, 2013
Mechanical Design 1.8m Data logging Labview VI Measured Output NDRE NDVI Plant Height Compensated A Non Compensated B In-situ Forage Yield Monitoring 64 66 2014 Explore new analysis method Improve mechanical design 55 Field ID Area (ha) Total Yield (Tonne) Based On Height Yield (tonne/hectare)/ (US ton/acre) 55 3.06 59.21 19.35/7.71 64 4.63 61.59 13.30/5.30 66 4.50 66.76 14.84/5.45
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