Precision Agriculture at: University of IL, TX A&M, and Oklahoma State University

1. Precision Agriculture at: University of IL, TX A&M, and Oklahoma State University Presented by: Keri D.Brixey UniversityOf Illinois

2. Precision Agriculture • What is it? • Audience Dependent • Different Places with Different Ideas • Common Goal: Being precise and accurate to maximize output while minimizing input (increasing efficiency).

3. University of Illinois Urbana- Champaign • Objectives: • “The mission of the laboratory is to conduct applied research, with industry and government agencies designed to develop "real world" applications of remote sensing for agribusiness.” • “To develop better control devices for field chemical applicators” • “To develop effective technologies to process the massive data set generated by precision farming production and research.” • “To develop and evaluate new ground-based sensing tools to increase data quality and validate remote sensing systems for precision farming.” • (http://www.age.uiuc.edu/remote-sensing/index.html)

4. University of Illinois Urbana- Champaign • Variable Rate Technology • Data Management for Site-Specific Farming • Sensing Systems for Precision Farming

5. Variable Rate TechnologyTo develop better control devices for field chemical applicators • Nozzle Fluid Dynamics Simulation • Variable Rate Nozzle • Smart Sprayer

6. Data Management for Site-Specific Farming • “In this study, they are optimizing both the sensing process and data to knowledge (D2K) conversion process.” • “Automatic and supervised learning processes have been applied on large database of agricultural crop systems.” • “Their objectives are to eventually understand the complicated system by means of processing a massive database with the state-of-the-art high performance computing systems.”

7. Sensing Systems for Precision Farming • Ground based sensing system • Hybrid positioning sensors • Agricultural remote sensing

8. Texas A&M Precision AG • Yield Mapping • Crop Height and Plant Population Sensor • Prediction of Nitrogen Stress using Reflectance Techniques • Integration of Remote Sensing with Crop Growth Models http://txprecag.tamu.edu/index.htm

9. Yield Mapping • “To develop a yield mapping system for cotton strippers using load cells to measure the weight increase of the basket during harvest.” • “Basket is weighed continually by suspending the basket on load cells at the basket pivots and support bar. Desired accuracy for each yield data points is +/- 1/4 bale/acre.” • “1997- the yield mapping system met this desired accuracy for 72% of the points.”

10. Exampleof Yield Map

11. Crop Height and Plant Population Sensor • “The sensor was fabricated to measure and store height data with the corresponding GPS position.” • “It is pulled behind a tractor and consists of mechanical switches and an infrared light bar.” • “Sensor was used throughout the summer to record plant heights. These heights were compared to hand measurements to determine its accuracy. These tests proved that the sensor could determine plant heights with reasonable accuracy.”

12. Crop Height and Plant Population Sensor (cont.) • Currently, tests indicate that the light sensor can be used to determine the population. • Now considering using a different light beam system with higher frequency capability to measure the populations. • Several different mechanical sensors are also being considered to determine populations and heights.

13. Prediction of Nitrogen Stress using Reflectance Techniques • “To develop a real-time multispectral sensor that can detect nitrogen deficiency in crops using spectral response from plant canopies.” • 2 different corn varieties are chosen. • 3 different levels of nitrogen treatment (adequate, moderate and low),all other nutrients are supplied equivalently for all treatment levels.

14. Prediction of Nitrogen Stress using Reflectance Techniques • A photodiode array is used to detect multispectral reflectance in the range of 400-1100 nm . • With the development of this sensor system, fertilizers could be applied at specific locations where they are needed rather than applying across the whole field. This could be one of stepping stones for eventual farm automation.

15. Integration of Remote Sensing with Crop Growth Models • The objective of this project is to demonstrate the use of remotely sensed data in crop growth models. • Crop models are able to predict the outcome of crops given the required input information. • Models serve as a surrogate for real experimentation. • By altering the input parameters and implementing the model over and over, users can identify the effect of individual input parameters. • A drawback to these models is due to weather data, soil physical properties, and soil fertility data, each requiring multiple collection methods.

16. Integration of Remote Sensing with Crop Growth Models (cont.) • Expensive and time-consuming collections • Remote sensing serves as a potential alternative to previous data collection methods, because of the ability to cover large areas in a short amount of time at a relatively low cost. • http://txprecag.tamu.edu/index.htm

17. Improving NUE with Precision Ag • Sensor Based Technology • VRT Equipment • Green Seeker-How it works http://www.dasnr.okstate.edu/precision_ag/

18. Our Goals: • 1. “To increase wheat and corn grain yields using less N and P fertilizers via the application of sensor-based nutrient management.” • 2. “To extend sensor-based nutrient management in the developed and developing world.” • 3. “To refine nutrient management algorithms specifically for wheat and corn.”

19. Improving NUE with Precision Ag • Crop reflectance measurements using optical sensors can be used to set more efficient and profitable fertilization levels. • The techniques that have been developed are appropriately applied at spatial scales of 1 m2 and will require optical sensor-equipped variable rate applicators. • The techniques rely on non-N limiting test strips in fields which allow an in-season estimate of fertilizer response. The use of NFOA may eventually replace N fertilization rates determined using production history (yield goals), provided that the production system allows for in-season application of fertilizer N. • Fertilizing each 1m2 area based on mid-season estimates of grain yield and the likelihood of achieving a response to added fertilizer could lead to improved NUE in cereal grain crops.

20. Sensor Based Technology • 1.  Early work using indirect measures to determine the N status of plants employed chlorophyll meters (SPAD 501 and 502) that measured transmittance at 430 and 750 nm.  • 2.   Plant tissue N and chlorophyll meter measurements have been found to be highly correlated, but they are crop and growth stage specific. • 3.  Once a reliable calibration between chlorophyll measurements and N status of the plant was established, N fertilization regimes were developed. • 4.  Similar to forage protein analysis using NIR, total N in growing plant tissue can be reliably detected from NIR measurements in the 780 and 1050 nm ranges.

21. Sensor Based Technology (cont.) • Initial results from sensor-based-variable-rate experiments by Stone et al. (1996) suggest that fertilizer N use efficiency can increase from 50 to 70% using this technology. This is largely because the sensors are capable of detecting large differences within extremely small areas (1m2) in an entire field. Instead of applying a fixed rate of 100 kg N/ha to a 100 ha field, this technology allows us to apply the prescribed amount to 1,000,000 individual 1m2 areas within the 100 ha field at N rates that range from 0-100 kg N/ha (and possibly more).

22. Interfering Agronomic Variables • Moisture availability (texture, water holding capacity) • Nutrient(s) deficiency(ies) and/or toxicity(ies) interactions • Crop and Variety within crop • Preplant N rate/Topdress N rate (yield goal) • Production system (forage vs. grain) • Tillage (background) • Weed interference/treatment (increased variability?) • Row spacing (coverage, plant density) • Resolution to be treated (field element size) • Cost of misapplication (economic vs. environment) http://www.dasnr.okstate.edu/precision_ag/

23. VRT Equipment 1 1-Our first variable rate applicator (4 independent sensors on the front integrated into 4 nozzle systems on the back) 1996. 2-Cooperative research program with CIMMYT. Kyle Freeman and Paul Hodgen have each spent 4 months in Ciudad Obregon, MX, working with CIMMYT on the applications of sensors for plant breeding and nutrient management. 3-GreenSeeker™ optical sensors and variable rate nozzles mounted on a 60-foot, self-propelled sprayer 2 3

24. Green SeekerHow it works • Optimizes nitrogen fertilizer application to meet plant needs. • Optically senses plant photosynthetic potential and living plant cell mass. • Computes total plant nitrogen and biomass. • Computes potential grain yield and additional nitrogen requirements. • Determines plant's response to additional nitrogen fertilizer. • Applies correct amount of nitrogen fertilizer.

25. Any Questions?????