Managing Global Resources for a Secure Future

2017 Annual Meeting | Oct. 22-25 | Tampa, FL

107813 UAS-Based Inspection of Seed Maize Production Fields in Northern Ghana.

Poster Number 1434

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: Agricultural Remote Sensing General Poster

Monday, October 23, 2017
Tampa Convention Center, East Exhibit Hall

Andrew Manu, Agronomy, Iowa State University, Ames, IA, Thomas Lawler, Iowa, Iowa State University, Ames, IA, Vincent Avornyo, Iowa State University, Ames, IA and Tara Wood, International Fertilizer Development Corporation, Accra, Ghana
Abstract:
Field inspection of maize fields and seed certification can be labor intensive and costly. The effort to control the various seed quality parameters imposed by national seed regulation/ certification could add as much as 25% to the cost of seeds. Although seed inspection and certification have both field and lab standards, a key process in effective seed certification starts with the standing crop in the field from planting to harvesting. For effective seed inspection and certification, factors like genetic purity, physical purity, seed health, crop rotation, isolation distance, and contamination need to be carefully evaluated. The Feed the Future Agriculture Technology Transfer Project (ATT) and implementation partners are developing an accelerated program for the application of Unmanned Aerial Systems (UAS) in agriculture and natural resource management. In 2016, the Iowa State Remote Sensing Team began collecting field data from October 1st – 4th using a fixed wing eBee Ag (senseFly, 2009 – 2017) and a Phantom 3 Pro quadcopter (DJI, 20017) in the Northern and Upper East regions of Ghana. The team carefully evaluated field size, isolation distance, estimated weed intensity, and crop health using the NDVI. The results showed that UASs have great potential as an unbiased alternative for seed inspection in Northern Ghana.

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: Agricultural Remote Sensing General Poster

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