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Big data” often refers
simply to the use of predictive analytics, user behavior analytics, or certain
other advanced data analytics methods that extract value from data, and not
just to a particular size of data set. Data tools can help determine changes
required to maintain yields and meet food demands
Potential of
Big Data in agriculture:
1) The
availability of data is on the grow because they are increasingly gathered by
cheap and numerous information-sensing mobile devices, aerial (remote sensing),
software logs, cameras, microphones, radio-frequency identification (RFID)
readers and wireless sensor networks.
2) In
agriculture, big data is often viewed as a combination of technology and
analytics that can collect and compile novel data and process it in a more
useful and timely way to assist decision making.
3)
Real-timeinsights to help performance optimisation advance analytics can show
how farmers are utilising their inputs and what adaptations are required to
take account of emerging weather events or disease outbreaks.
4)
Consideringthe increasing labour shortages in the sector the capacity for big
data analysis that lessens the need for physical manpower is of great advantage
for agriculture.
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Role of Big
data in Agriculture:
1)
Developmentof new seed traits: Access to the plant genome with new ways to
measure, map and drive information betters products.
2)
SeedSelection: Big-data businesses can analyse varieties of seeds across
numerous fields, soil types, and climates and select the best.
3) Cropdisease:
Similar to the way in which Google can identify flu outbreaks based on where
web searches are originating, analysing crops across farms helps identify
diseases that could ruin a potential harvest.
4) Irrigation
Precision agriculture aids farmers in tailored and effective water management,
helping in production, improving economic efficiency and minimising waste and
environmental impact.
5) Weather
Advanced analytics capabilities and agri-robotics such as aerial imagery,
sensors help provide sophisticated local weather forecasts can help increasing
global agricultural productivity over the next few decades.
6) Climatechange:
Since, climate change and extreme weather events will demand proactive measures
to adapt or develop resiliency, Big Data can bring in the right information to
take informed decisions.
7)
Foodtracking: Use of sensors and analytics to prevent spoilage and food-borne
illnesses.
The big data
revolution is in its early days and most of the potential for value creation is
still unclaimed.
But it has set the industry on a path of rapid change and
new discoveries. Stakeholders committed to innovation will likely be the first
to reap rewards. If the farmers would have been concerned about the infirmities
in terms of data-based farming, production could be increased.
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