To meet global food demand by 2050, agriculture efficiency must increase by 35% – 70% and technology is the key. at Farmkonnect we believe the world doesn’t need more farmers rather we need more efficient farmers. Big Data revolution together with machine learning and Artificial intelligence would help farmers make better decisions and bring about an astronomical improvement in Nigerian agriculture.
For farm produce to get to the table, it has to travel through multiple intermediaries, involving multiple variables and hence multiple decision making at every single level. At the same time, at every single level, enormous data is generated. Hardly are these data collected, stored or processed for decision making. Here comes the role of ‘Big data’ in Agriculture. Big data not only refers to data itself but also set of technologies that capture, store, manage, analyse – large and variable collections of data to solve complex problems. Hence, Big Data is envisaged to solve enduring problems and future menaces in agriculture.
Data generation is the first step to utilize Big data in Agriculture. There are generally two major sources of data generation in agriculture 1. Human-generated, 2. Machine generated. Human-generated involves traditional data that is recorded in any farm like purchasing inputs, fertilizer application schedule, yield, etc., It has to be collected from individual farms, then structured and stored in database systems. This could be done in Nigeria through a wide network of extension workers or agricultural officers at the grassroot level.
Second, Machine generated (MG) data, as the name denotes, it is obtained from simple sensor records to complex computer logs. Nowadays, MG data is increasingly available through the adaptation of smart farming and the Integration of IoT. Farms using drones, infrared cameras, GPS augmented machines and satellite monitoring. These data form an important MG structured information for big data aided farming.
Applications of ‘Big Data’ in Agriculture
The voluminous data available could be used primarily for crop forecasts like meteorological data for weather predictions assisting sowing or other intercultural operations. Cropin, an Indian based company provides satellite-based geo-tagging of plots which ensures accurate prediction with real-time updates to track the various stages of crops get visual confirmation, timely alerts and customized analysis on how crops performed by zone, input, and more.
On the other end, it is also used for Market forecasts ensuring better prices for farm produce. In a similar way, ‘Farmer Business Network’, pools data from numerous small farmers and in turn shares the insights to its members. They provide information on yields, supply prices and other information that help small farmers to compete with larger ones.
Big Data and IoT (Internet of Things) work in conjunction, as the latter serves as a means of gathering data. Nowadays IoT has revolutionized the way through which agricultural machinery and farm tools are used for sending and receiving the data. Precision farming is exploiting this advantage and through this focus is shifted to per plant productivity rather than productivity per unit of area. Putting this into operation, a start-up ‘AGER point’ in Florida that produces nuts and citrus orchard management software using satellite data. This provides tree specific information and boosts production per plant by accessing their needs individually.
As the Nigerian government dreams for doubling farm income, in which raising productivity has been given primary impetus, the trio – Big Data, smart farm machinery and IoT would help to achieve the dream