Scaling Up Agricultural Research With Artificial Intelligence

被引：16

作者：

Bestelmeyer, Brandon T. ^{[1
]}

Marcillo, Guillermo ^{[2
]}

McCord, Sarah E. ^{[1
]}

Mirsky, Steven ^{[2
]}

Moglen, Glenn ^{[2
]}

Neven, Lisa G. ^{[3
]}

Peters, Debra ^{[1
]}

Sohoulande, Clement ^{[4
]}

Wakie, Tewodros ^{[3
]}

机构：

[1] USDA ARS, Jornada Expt Range, Las Cruces, NM 88001 USA

[2] USDA ARS, Beltsville Agr Res Ctr, Beltsville, MD 20705 USA

[3] USDA ARS, Temperate Tree Fruit & Vegetable Res Unit, Wapato, WA USA

[4] USDA ARS, Washington, DC 20250 USA

来源：

IT PROFESSIONAL | 2020年 / 22卷 / 03期

基金：

美国国家科学基金会;

关键词：

Agriculture; Soil; Biological system modeling; Predictive models; Data models; Vegetation mapping;

D O I：

10.1109/MITP.2020.2986062

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Agricultural systems are enormously variable in space and time. New and developing artificial intelligence (AI)-based tools can leverage site-based science and big data to help farmers and land managers make site-specific decisions. These tools are improving information about soils and vegetation that forms the basis for investments in management actions, provides early warning of pest and disease outbreaks, and facilitates the selection of sustainable cropland management practices. Continued progress with AI will require more observational data across a wide range of agricultural settings, over long time periods.

引用

页码：33 / 38

页数：6

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