Big Data Driven Agriculture: Big Data Analytics in Plant Breeding, Genomics, and the Use of Remote Sensing Technologies to Advance Crop Productivity. Issue 1 (1st May 2019)

• Record Type: Journal Article
• Authors: Shakoor, Nadia; Northrup, Daniel; Murray, Seth; Mockler, Todd C.
• Is Part Of: Plant phenome journal. Volume 2:Issue 1(2019)
• Page Start: 1
• Page End: 8
• Publication Date: 2019-05-01
• Subjects: Phenotype -- Periodicals; Plant genetics -- Periodicals; Periodicals; 581.35
• DOI: 10.2135/tppj2018.12.0009
• Languages: English
• ISSNs: 2578-2703

Abstract

Abstract : Core Ideas: Interdisciplinary efforts in high‐throughput field phenotyping Linking proximal and remote field phenotyping Cyberinfrastructure for high‐throughput field phenotyping Plant breeding and agronomy are labor‐intensive sciences, and the success of these disciplines is critical to meet planetary challenges of food and water security for the world's growing population. Recent gains in sensor technology, remote sensing, robotics and autonomy, big data analytics, and genomics are being adopted by agricultural scientists for high‐throughput phenotyping, precision agriculture, and crop‐scouting platforms. These technological gains are ushering in an era of digital agriculture that should greatly enhance the capacity of plant breeders and agronomists. This report encompasses the priorities and recommendations that emerged from two USDA National Institute of Food and Agriculture (NIFA)‐funded Big Data Driven Agriculture workshops held on 26–27 Feb. 2018 in Arlington, VA. The objectives of the workshops were to bring together diverse subject‐matter experts in the represented disciplines of plant breeding, machine learning, remote sensing, and big data infrastructure and analytics to (i) explore how large and comprehensive datasets in plant breeding, genomics, remote sensing, and analytics will benefit agriculture; (ii) discuss strategies for creating a successful field phenotyping campaign and to determine protocols for the collection and analysis of agricultural big data; (iii) consider how to best engage the broader community of public and private plant breeders and agronomists to determine additional challenges, make wider use of the data, and ensure application of standardized methods to other datasets; and (iv) generate a report describing cross‐cutting short‐ and long‐term funding needs for continued success in this domain.