Data Analytics Transforming the Agriculture Industry
Agriculture has moved on a long way from its traditional roots to the modern agriculture. As an industry, it has been evolved from a stage where it depended merely on submissions from soul farmers to a modern, data driven venture. Nowadays farmers are ready to tackle insights protected with a lot of historical data to return to a conclusive analysis on the crop to be planted and therefore the cultivation method to be used.
Data
Analytics is now suffocating old age agriculture processes to streamline
irregularities and increase efficiency in cultivation, irrigation, harvesting,
supply chain management, and logistics to make sure that there is a little to
no risk involved when handling biodegradable goods.
The scope of massive Data Analytics in Agriculture
lifecycle:
IoT, Big
Data, and Cloud computing are transforming the way agriculture functions as
an Industry in India and around the world. Data
Analysis in agriculture globally is valued currently at 565 million USD,
and therefore the projected valuation by 2023 is 1256 million USD.
Data
Analysis in agriculture is being exploited to amend every step in the
agriculture lifecycle to be more cost-effective and efficient. From crop
selection, cultivation method, harvesting, and provide chain management, the
impact is being felt at every stage of the worth chain.
With the utilization of sensors and connected devices
interrelating with one another on the farm, farm owners and managers are now
equipped with volumes of crop data in real time to guide farmer’s actions. Big
data in agriculture is transforming livestock care, developing efficient risk
assessment modules, democratizing the potential of urban farming, and
catalyzing efficient use of resources (land and labour).
Some major benefits of applying data processing and
machine learning techniques in agriculture:
Improved crop management:
With intuitive crop data, farmers can make informed
decisions on the type of crop which is to be grown, to choose a strain which is
best suited for the atmospheric conditions, rain seasons, and therefore the
sort of soil to form a profitable harvest. Hybrid varieties or breeds that are
most suited to the soil and climate are often recommended supported data
analysis that are most contrary to diseases and breakdown.
Better risk assessment:
Risk within the agriculture sector is unavoidable, but the
power to predict and manage the danger at every stage of the lifecycle makes
the farmer better equipped to require calculated decisions. Big Data and Cloud
computing utilizes data from Google Earth, global weather, and data fed in by
the farmer to project a roadmap that helps farmers plan the journey right from
crop selection to distribution. It also factors within the local market prices,
natural calamities, pest infestation etc. that might increase or decrease the
worth of commodity and hardships that a farmer could face with reference to
supply chain management. Data aids farmers in making decisions which would help
them stay away from potential high-risk scenarios in the lifecycle of the crop.
Efficiencies in supply chain:
Supply chain management is not simply about distributing the
finished goods to the desired market anymore. With data
analytics, farmers are now empowered with insights that can help them
predict the market conditions, consumer behaviour towards the finished goods,
factor-in inflation, and other variables that will help them plan the whole
process even before sowing the seeds. This becomes a salient insight because it
allows farmers to manage the conditions that enable them to maximise return on
investment and mitigate any unnecessary loss.
The Bottom line:
Supply chain management isn't almost distributing the
finished goods to the specified market anymore. With data
analytics, farmers are now authorized with insights which helps them
predict the market conditions, consumer behaviour towards the finished goods,
factor-in inflation, and other variables that will help them plan the whole
process even before sowing the seeds. This becomes a salient insight because it
allows farmers to manage the conditions that enable them to maximise return on
investment and mitigate any unnecessary loss.
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