"Today, India ranks second worldwide in farm output. The economic contribution of agriculture to India's GDP is steadily declining with the country's broad-based economic growth. Still, agriculture is demographically the broadest economic sector and plays a significant role in the overall socio-economic fabric of India." - From Wikipedia
Why this motivation?
I am seeing huge potential for farmers to dictate the sale of their data to large agricultural companies (for statement and research purpose). But our technological goal is to get the revenue stream back to the growers. Because I know what their potential are.
While surfing on the internet I came to know that some companies provides farmers with a device that can be plugged into a tractor to gather data on land and machinery which collects the data in a structured and in unstructured manner including fuel economy, speed, direction and products being applied. These static, sensor based unstructured information is then directed to an electronic file were accessed and quanitified using Bigdata tools and can be analyzed using data science strategies.
Once these kind of data are gathered, using supply chain management strategies (as I mentioned on my earlier post Bigdata Analytics in Supply Chain Management) that facilitates opportunities and turns between farmers and individual stake-holders or agricultural companies.
What will be benefit for farmers?
Agricultural companies can make offers to purchase the farmer's data (structured / unstructured, it doesn't matter) because these are going to be processed under Bigdata tools and insights are to be gathered from quantified sources by means of effective data science techniques. There is a chance that farmers can make profit on their agricultural data.
Agriculture technology experts are addressing how farmers can decide and icrease their revenue from their farm's data.
What I am trying to say here?
We are trying to mix and match technology both old and new to boost agricultural production sustainably in the years ahead.
When seeing some citations as like below from external sources from the internet, the confidence of using Big data and data driven analytics in agriculture is increasing.
"In precision agriculture, control centers collect and process data in real time to help farmers make the best decisions with regard to planting, fertilizing and harvesting crops. Sensors placed throughout the fields are used to measure temperature and humidity of the soil and surrounding air. In addition, pictures of fields are taken using satellite imagery and robotic drones" - From Internet
“A farmer could take a picture of a crop with his phone and upload it to a database where an expert could assess the maturity of the crop based on its coloring and other properties. People could provide their own reading on temperature and humidity and be a substitute for sensor data if none is available,” - From Internet
So, by reviewing the past data on agriculture and farming activities in developing countries, assesses and provides the necessities and requirements required at various levels to benefit from Big Data.
Efficient analysis reveals some insights on pattern of Big Data and its effective use in key development areas. These insights can be used to discuss the outcomes over developing countries and facilitates to learn from the utilization of Big Data in big corporations as well as in other green-activities in industrialized countries.
The agricultural research community not only needs to build its own Bigdata and data management infrastructures, but also to seek effective analytical techniques to extract information from the large volume of agricultural unstructured data.
So, current technology is capable and provides lots of tools and facilities for computational and analytical solutions for the integrity check and their analysis of large, uncommon unstructured datasets on the Big-Data scale.
What will be result?
"Future begins here"
Now I am writing Bigdata and datascience articles on what to do, on upcoming days I am going to highlight the tools and analytical techniques for capturing and analyzing volumes of data with examples on respective domains.
Image courtesy: images.google.com