Data tools can help determine changes required to maintain yields and meet food demands
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.
Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning statistics and database system.
Precision agriculture’s main objective is to ensure profitability, efficiency, and sustainability using the big data gathered to guide both immediate and future decision-making. This could cover everything — from when it is best to apply fertilizers, chemical and seeds, to from where in the field it is best to apply a rate.
According to agriculture funders, the big data practice comprises capturing relevant data from a huge number of sources, collecting it today and translating it into actionable information to improve business processes and solve problems at scale and speed.
Real-time insights 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.
To achieve this, advanced algorithms are needed to swiftly unlock the highly valuable insights for products to perform well on an ongoing basis despite changing conditions. The development of highly-specific customer segmentation set has become possible to tailor product offerings to meet customer needs.
For instance, if Black Grass becomes problematic in a given region, suppliers can deploy big data techniques such as real-time micro-segmentation of customers to target promotional and marketing activities, thus facilitating better utilisation of marketing spends. Big data connectivity has proven itself a key asset for companies seeking a competitive advantage over their competitors.
Benefits include faster unearthing of valuable insights and the ability to develop and adapt products that meet specific customer needs on an ongoing basis.
Robots can play an important role in control, but it can be expected that the role of humans in analysis and planning is increasingly assisted by machines so that the cyber physical cycle becomes almost autonomous.
Considering its potential, various agri-tech companies are providing their services to the producers to make the practice more approachable and available. Hardware-wise there are various sensors collecting the available data.
In this range, we can encounter autonomous vehicle devices farmers place in the ground to measure soil moisture and nutrient, predictive weather stations and image-capturing satellites and drones mapping out land and measuring crop health.
