Promoting Food Security through Improved Analytics. (2015)

• Record Type: Journal Article
• Authors: Gounden, Colin; Irvine, John M.; Wood, Richard J.
• Is Part Of: Procedia engineering. Volume 107(2015)
• Page Start: 335
• Page End: 336
• Publication Date: 2015
• Subjects: Analytics -- precision agriculture -- Bayesian networks -- crop yield -- food security -- modeling -- machine learning; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2015.06.089
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Food and agriculture are growing issues due to climate change, flattening yield productivity, and the link between food availability and national security. Most technology and scientific development related to improving agricultural yield has been very narrowly focused on a specific area such as sensors or imaging or genetics. In this paper, we argue that the technology to improve yields already exists and that the most important requirement is to advance the integration and analysis of multi-modal data such as meteorological, sensor, image, and genetic information in real-time in a distributed (lab to farm) manner. We examine multiple trends related to processing, storage, imaging, genomic and proteomic data, and farming technology. By analyzing the relationship of these trends we provide a set of foundational needs or focus areas beyond simple predictive algorithms that are necessary to improve yields. This paper shows that we are relatively close to being able to increase crop yields and improve food production through better use of existing technologies.