211
The lower left hand corner of Fig. 8.1 shows the most versatile sensor in the
world – individuals using their cell phone. Particularly for agriculture in developing
nations, the cell phone is a phenomenal source of potential change – because of both
information sent to those individuals and information they now can provide.
As illustrated in the lower right hand quadrant of Fig. 8.1, satellite imagery can
measure temporal changes in reflectivity of plants to provide estimates of growth
(RIICE 2013). The picture is focused on rice production in Asia. Such information
has numerous potential uses. One is to provide a low-cost means of identifying
fields where adverse conditions have caused major production shortfalls. Once that
field is identified, similar low-cost means could be used to provide insurance payments to farmers eligible for that insurance.
While satellite imagery is one source of remotely sensed data, recent years have
seen a pronounced increase in the capabilities and interest in unmanned aerial systems (UASs) as a source of data for agriculture. There are numerous ongoing efforts
to transform UAS technology originally focused on military purposes to applications supporting production agriculture. “Universities already are working with
agricultural groups to experiment with different types of unmanned aircraft outfitted
with sensors and other technologies to measure and protect crop health” (King
2013). A few, of many, example applications include the following:
• Monitoring of potato production (Oregon State University)
• Targeting pesticide spraying on hillside vineyards (University of California, Davis)
• Mapping areas of nitrogen deficiency (Kansas State University)
• Detecting airborne microbes (Virginia Polytechnic Institute and State University)
8.2.3 Technologies Transforming What Can Be Data?
Terms such as precision agriculture, big data, digital technology, and big data analytics are frequently used in society and among farmers. While such use is common,
a common understanding of what these terms precisely mean has not been achieved.
(Because of the rapidly evolving nature of the technologies, the problem is not the
lack of definitions, rather it is that numerous definitions, all with some validity, exist.)
This section will provide a brief perspective of the terms digital technology, precision agriculture, and big data analytics. The intent is not to provide precise or
universal definitions. Instead, the goal is to provide a general perspective that will
contribute to a better understanding of the chapter’s contents. Further discussion
and example applications are included later in this chapter.
The following two-part explanation of digital technology often is useful:
1. Digital technology in agriculture involves:
• Employing sensors and technologies to capture digital data and operating
machines which use digital information to differentially apply inputs
8 Digital Technologies, Big Data, and Agricultural Innovation
The lower left hand corner of Fig. 8.1 shows the most versatile sensor in the
world – individuals using their cell phone. Particularly for agriculture in developing
nations, the cell phone is a phenomenal source of potential change – because of both
information sent to those individuals and information they now can provide.
As illustrated in the lower right hand quadrant of Fig. 8.1, satellite imagery can
measure temporal changes in reflectivity of plants to provide estimates of growth
(RIICE 2013). The picture is focused on rice production in Asia. Such information
has numerous potential uses. One is to provide a low-cost means of identifying
fields where adverse conditions have caused major production shortfalls. Once that
field is identified, similar low-cost means could be used to provide insurance payments to farmers eligible for that insurance.
While satellite imagery is one source of remotely sensed data, recent years have
seen a pronounced increase in the capabilities and interest in unmanned aerial systems (UASs) as a source of data for agriculture. There are numerous ongoing efforts
to transform UAS technology originally focused on military purposes to applications supporting production agriculture. “Universities already are working with
agricultural groups to experiment with different types of unmanned aircraft outfitted
with sensors and other technologies to measure and protect crop health” (King
2013). A few, of many, example applications include the following:
• Monitoring of potato production (Oregon State University)
• Targeting pesticide spraying on hillside vineyards (University of California, Davis)
• Mapping areas of nitrogen deficiency (Kansas State University)
• Detecting airborne microbes (Virginia Polytechnic Institute and State University)
8.2.3 Technologies Transforming What Can Be Data?
Terms such as precision agriculture, big data, digital technology, and big data analytics are frequently used in society and among farmers. While such use is common,
a common understanding of what these terms precisely mean has not been achieved.
(Because of the rapidly evolving nature of the technologies, the problem is not the
lack of definitions, rather it is that numerous definitions, all with some validity, exist.)
This section will provide a brief perspective of the terms digital technology, precision agriculture, and big data analytics. The intent is not to provide precise or
universal definitions. Instead, the goal is to provide a general perspective that will
contribute to a better understanding of the chapter’s contents. Further discussion
and example applications are included later in this chapter.
The following two-part explanation of digital technology often is useful:
1. Digital technology in agriculture involves:
• Employing sensors and technologies to capture digital data and operating
machines which use digital information to differentially apply inputs
8 Digital Technologies, Big Data, and Agricultural Innovation