Opportunities and Challenges for Remote Sensing in Agricultural Applications of Data Science
published: Nov. 7, 2016, recorded: August 2016, views: 1194
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Increases in global population, coupled with challenges of climate change require development of technologies to support increased food production throughout the entire supply chain – from plant breeding to delivery of agricultural products. Developments in remote sensing from space-based, airborne, and proximal sensing platforms, coupled with advanced capabilities in computational platforms and data analytics, are providing new opportunities for contributing solutions to address grand challenges related to food, energy, and water. Spaceborne platforms carrying new active and passive sensors are moving from complex, multi-purpose missions to lower cost, measurement specific constellations of small satellites. Advances in materials are leading to miniaturization and mass production of sensors and supporting instrumentation, resulting in advanced sensing from affordable autonomous vehicles. New algorithms to exploit the massive, multi-modality data sets and provide actionable information for agricultural applications from phenotyping to crop mapping and monitoring are being developed. An overview of recent contributions, as well opportunities and challenges for data science in analysis of multi-temporal, multi-scale multi-sensor remotely sensed data will be presented.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !