04 May Best Practices for NDVI Crop Health Sensors – A White Paper
NDVI Sensors: Knowing Where to Start
Unmanned aircraft (drones) have become a popular and effective tool for monitoring vegetation, analyzing crop health, and predicting yields. Within minutes, NDVI sensors can be used to collect overhead imagery of fields to provide actionable data to the grower. If the NDVI crop health imagery is accurate and used properly, it can produce significant savings and increased yields for growers. However, with a large number of agriculture-related drone products on the market, it can be difficult to compare capabilities and determine which systems provide the most accurate and reliable data.
Best Practices — What to Look For
This white paper focuses on NDVI sensors, investigating a common deficiency of many of these drone systems and highlights what features should be sought when selecting a system or camera.
In this white paper you’ll learn about:
- Vegetative Health Indices
- Commercial Sensors
- Camera Modifications
- How NDVI is calculated
- The source of errors
- Precision filtering
Now is the Time
As we enter the 2017 North American growing season, now is the time to gain insight and understand what to look for when selecting a NDVI sensor.
If you have any questions or would like to discuss specific solutions, we’d love to hear from you! Email us or call 844-SENTERA (736-8372).
About the Author:
Ryan Nelson, Chief Mechanical Engineer for Sentera, LLC, has been developing advanced UAV sensor and mechanical systems for over 13 years.
Preceding the formation of Sentera, Ryan held positions at several large aerospace companies such as Lockheed Martin, Goodrich Corporation, UTC Aerospace Systems, as well as small precision agriculture companies. While at Lockheed Martin, Ryan pioneered the Desert Hawk III unmanned system and went on to lead the mechanical design of many “industry-first” sensor systems.
Other career accomplishments include designing and producing industry-leading sensors such as the Micro EO and IR Gimbal, Lockheed Martin Indago Mapping Gimbals, and the Sentera Single, Double 4K, and Quad Multispectral agriculture sensors.
At Sentera, Ryan focuses on the design and manufacturing of advanced, precision agriculture sensor technology.
Ryan holds a bachelor’s degree in Mechanical Engineering from Iowa State University.