GIS: The Systematic Approach to Precise Farm Management Robert Biffle Precision Agriculture April,

Statement of the Problem In the ever changing markets of today’s economies, data needs to be processed into information quickly and scientifically while being transferred to decision makers in a timely manner. Decision makers can then make decisions that are timely and accurate for their application specific situation, which in our case is farm management.

Project Goals ► “Accurate and precise” collection of field data using Calibrated Instruments ► Use of technology (i.e., GIS systems) to quickly and carefully process data into information. ► Using technology to apply information to gain knowledge about in-field phenomena. ► Increase profit and decrease cost for the operation through better management practices

Methodology: Data Sources ► 1 meter imagery, courtesy of SST and the Certified Reflectance Imaging System (CRIS) project ► 1 meter DOQQ and soil types map from the USGS ► Base data (state and county maps) from ESRI

Methodology: Data Preparation ► Visually inspect data for errors during collection ► Use SSTReflector program to calibrate (remove irradiance) on remotely sensed images ► NDVI creation and scaling with PCI software ► Surface generation and information analysis using SSToolbox

Visual Inspection of Camera File Transposed Data

Reflected Light Incoming Light Camera Radiometer Using radiometers in the field to capture incoming light, takes the place of older tarp methods which are more costly and cumbersome to use

Visual Inspection of Radiometer File

Data Preparation: Images Original TIFF Image (ODR)

Image Reflection Calculations

Normalized Reflectance Image

NDVI Creation ► Using the standard Normalized Difference Vegetative Index (NDVI) equation (NIR-R) / (NIR+R) yields the single band TIFF image ► Calibration of NDVI to a standard scale with the following equation (NDVI + 1) * 100 ► This creates a scale from 0 to 200 which can be compared to other NDVI images created with the same methodology

Scaled NDVI Image White dots are trees and higher NDVI values Lower NDVI values

Surface Creation ► Using ToolBox the NDVI image can be made into a surface, from which further informational analysis can be achieved.

Management Zone Creations ► Using the NDVI surface, Toolbox can create management zones (based on NDVI).

Surface Creations cont. ► With the continual collection of data, time series analysis can be preformed. ► The same procedures were used for both dates of the NDVI surface creations ► The difference NDVI or time series surface is made by subtracting the NDVI values from each date

NDVI Difference Surface

USGS General Soil Types Map

Bad Soil Areas

Low Reflectance

LOW NDVI

Problem Verification in the Field Actual bad trees in the problem areas of the field pictured below

Interpretation of Results ► Normalization of the NDVI images allows the time series and a safe method of comparing digital numbers over time ► Combination of imagery, soil types, and in field verification can greatly increase management information and decision making accuracy and/or verification of current management practices ► “Problematic areas” identifiable on final map as well as imagery and NDVI surfaces ► Imagery must have field verification to yield the most useful information

Project Concerns ► Scientific research and development is a costly venture and takes capital ► NDVI, soil data, and hand held recordings still need field verifications such as actual yield amounts, and inspection of trouble spots identified by imagery ► NDVI alone is hard to trouble shoot what is going on in the field, this is where the trained human brain will prevail

Conclusion ► Imagery can assist in the monitoring of in-field variation, and allow early detection of plant stress ► Imagery data alone is not sufficient for a management program, there are too many factors at work and the resolution is not as good as hand held sensors ► Although, the cost is cheaper, and in high end crops such as pistachios, strawberries, lettuce, tomatoes, and citrus, the added cost of imaging is lessened by the amount of land flown

Conclusion cont. ► High end crops can be monitored weekly by aerial flights like CRIS ► Risk in farming is guaranteed, but imaging can help tilt the table in the farmers direction with early detection of plant problems identifiable by NDVI ► GIS is the key to combining and comparing sources to derive a new product to assist management decisions ► GIS allows its users to store, organize, and analyze, large amounts of data it over time ► The recording and storing of data allows for the betterment of decisions making based on the amount of information that can be obtained.

QUESTIONS COMMENTS