Imagine a farmer who’s about to till his farmland, but has no idea when the rains will arrive. However, instead of brooding any further, he uses a mobile app to figure the precise date and time of rainfall. Next, he also enquires about the expected amount of rainfall, soil moisture, and productivity to measure if his fresh seeds should be sown at all. Upon inspection, he decides against it and saves himself from loss.
By the year 2050, as the world hits a population of over 9 billion, the hunger gap is expected to widen. Scientists believe that the only measure to combat this problem lies in greater agriculture produce—a process fraught with uncertainties. In their quest to improve productivity, agriculturalists are increasingly looking towards big data for food security.
Big data farming, also called ‘precision farming’, is expected to play a range of significant roles from offering weather forecasting, real-time optimization of farming machinery, cloud-hosted information resources for farmers, automated irrigation recommendations, monitoring of grain prices, and management of inventories and budgets using mobile-based technologies. Irrigation scheduling based on highly accurate weather forecasts and real-time field data will also optimize decision making and consequently reduce resource use. Having access to such forecasts and field data on a mobile platform even enables farmers to take decisions on the go. In fact, organizations in the United States already operate cloud-based farming information systems that use weather measurements and soil observations to predict weather for the next seven days.
Investing in sophisticated farming tools might not be sufficient to address the problem of food scarcity. This is where big data steps in. It not only promises to optimize and track harvests, but also has the potential to increase yield production directly. Modern data-gathering technologies are expected to prove to be transformative, enabling farmers to generate better agricultural produce.
With specific crop history data on every field in the country, agriculturalists can now predict their potential harvests better. In fact, farmers and agriculturalists could also benefit from big data to monetize crop yields. By aggregating local pricing in real time and automatically computing transportation costs, farmers can get the best prices for their products without a mediator. What used to take them a couple of hours a day, could now be accomplished in minutes. Thus, precision farming applies not just to the process of farming, but also up to the point where the products are delivered to its end customer. For instance, an IT services company in India has developed a system that monitors conditions such as temperature, humidity, and oxygen levels of food shipment containers in India to monitor and maintain the quality of agricultural produce.
How do you think big data will assist agriculture to create more powerful farming processes? Share your views with us in the comments box below.
Big Data,farming data,precision farming