plant density, new farming practices, improved pest and disease control, more
precise application of plant fertilizer, and advances in soil and water management
will be realized by applying new knowledge and new technologies.
8.1.2 The Role of Big Data in Agriculture
In recent years, digital revolution is transforming modern farming (Bronson and
Knezevic 2016). Nowadays farmers can easily look for answers and solutions to
issues met in farming via online services provided by professional consulting
companies or local government departments. Even smallholder farmers are exercising new precision agricultural equipment like unmanned airborne vehicles (UAVs)
to gather information. Many farmers make their decisions based on the data and
information provided by authentic sources. Conventional tractors are equipped with
fancy cameras and sensors which can stream live data about soil and crops to
agricultural information companies or agricultural statistics services for providing
better real-time field or even location-specific decision suggestions to farmers.
With the rapid development of sensor, sensing, and data collecting technologies,
the capabilities of human society to collect data have been expanded exponentially.
A huge amount of data have been collected by government agencies, organizations,
industries, and individuals. The development of data interoperability technology and
wide adoption of open data policy have made the data more accessible with low or
no cost. However, the data are very diverse in terms of sources, format, quality, etc.
The human society lacks enough experience and knowledge in managing and
exploring the rapidly increasing volumes of data. As the result, most of those
collected data are either discarded or put into archives without being fully utilized,
although the data may contain information and knowledge that are valuable to the
socioeconomic activities of the society.
The agricultural sector actually has accumulated mountains of records in its long
history, many of which, however, are not digital. Big data is different from the
historical information gathered in old fashion. Modern computer science and sensor
technology can help us better understand the complex interactions among the natural
and social components of agricultural systems. Currently, a huge number of in situ,
airborne, and space-borne sensors monitor continuously the agricultural systems and
related physical environment and produce big quantities of data in an unprecedented
pace. How to process the big data to extract valuable information is the first
challenge we have to face. Although there is already a large number of tools and
services designed for managing and processing big data, they have not yet been
widely applied in agriculture (Kamilaris et al. 2017). Based on existing researches,
the analysis of agricultural big data promises great opportunities for improving
agricultural productivity and sustainability. The availability of related hardware,
software, the openness of data sources, and farmer involvement shall encourage
big data research and practice in agriculture.
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precise application of plant fertilizer, and advances in soil and water management
will be realized by applying new knowledge and new technologies.
8.1.2 The Role of Big Data in Agriculture
In recent years, digital revolution is transforming modern farming (Bronson and
Knezevic 2016). Nowadays farmers can easily look for answers and solutions to
issues met in farming via online services provided by professional consulting
companies or local government departments. Even smallholder farmers are exercising new precision agricultural equipment like unmanned airborne vehicles (UAVs)
to gather information. Many farmers make their decisions based on the data and
information provided by authentic sources. Conventional tractors are equipped with
fancy cameras and sensors which can stream live data about soil and crops to
agricultural information companies or agricultural statistics services for providing
better real-time field or even location-specific decision suggestions to farmers.
With the rapid development of sensor, sensing, and data collecting technologies,
the capabilities of human society to collect data have been expanded exponentially.
A huge amount of data have been collected by government agencies, organizations,
industries, and individuals. The development of data interoperability technology and
wide adoption of open data policy have made the data more accessible with low or
no cost. However, the data are very diverse in terms of sources, format, quality, etc.
The human society lacks enough experience and knowledge in managing and
exploring the rapidly increasing volumes of data. As the result, most of those
collected data are either discarded or put into archives without being fully utilized,
although the data may contain information and knowledge that are valuable to the
socioeconomic activities of the society.
The agricultural sector actually has accumulated mountains of records in its long
history, many of which, however, are not digital. Big data is different from the
historical information gathered in old fashion. Modern computer science and sensor
technology can help us better understand the complex interactions among the natural
and social components of agricultural systems. Currently, a huge number of in situ,
airborne, and space-borne sensors monitor continuously the agricultural systems and
related physical environment and produce big quantities of data in an unprecedented
pace. How to process the big data to extract valuable information is the first
challenge we have to face. Although there is already a large number of tools and
services designed for managing and processing big data, they have not yet been
widely applied in agriculture (Kamilaris et al. 2017). Based on existing researches,
the analysis of agricultural big data promises great opportunities for improving
agricultural productivity and sustainability. The availability of related hardware,
software, the openness of data sources, and farmer involvement shall encourage
big data research and practice in agriculture.
146
L. Di and Z. Sun