May 11 2016

Revolutionizing the Agriculture industry – Applying Analytics on Internet of Things

Analytics on Internet of Things

Internet of Things (IoT) and Big Data analytics could be the two most buzzed terms in the Industries for the past 2 years. IDC has forecasted to yield $8.9 trillion revenue by 2020 in IoT and Goldmann Sachs has estimated that 28 billion devices will be connected to internet by 2020, which indicates that each of this connected devices will shoot back with humongous amount of data each second, therefore you need a proper analytical tool/solution for the “Value creation”.

Introduction to IoT:

IoT is a network of inter-connected objects able to collect and exchange data. This eco-system enables entities to connect to, and control, their devices. The information that performs the command and/or sends back the information back over the network to be analyzed and displayed on the remote. For example: At 6am John’s phone receives a mail informing that his meeting has been pushed back, now his mail service tell his smart clock to give him an extra 30 minutes of sleep and alerts him to the change once he wakes. This is how the whole system works, but with the addition of Analytics to this prototype will enable the firms to take an efficient and best decision with the available data.

Analytics on IoT in Agricultural Industry:

How will this trend influences agriculture industry? The answer is, it is going to entirely revolutionize the whole working pattern of the industry rather than just influencing. Because in 2050 global population is expected to reach 9 billion people (34% higher than today), in parallel food production should be increased by at least 70%, but in other hand U.S Department of Agriculture states that 90% of all crop loses is due to weather related incidents. Thus, if we could minimize the losses due to weather related incidents and use the limited fresh water resource as draught is prevailing across the globe (to the fact 70% of the world’s fresh water is already being used for Agriculture). And so, this issues can be resolved by using predictive Analytics, which is going to play a major part in building value creation. Predictive analytics acts as a central element in predicting the future picture which requires a lot of input data from various distinctive variables. The basic idea is to identify and differentiate between the high and low yielding crop lands by measuring its productivity.

Flint River Valley project:

Hyper local forecasting techniques will assist farmers to overcome the above obstacles. The Flint River Valley is a part of Georgia’s agricultural industry, it roughly contributes around $2 billion annually in farm-based revenue. A pilot test run is been made in Flint River valley in USA, by researchers from the Flint River Soil and Water Conservation District, the U.S. Department of Agriculture, the University of Georgia and IBM. The primary objective is to give farmers a beneficial information about the weather through analyzing the data obtained from various sensors (fig: 1.1) which is installed over fields. Here sensors collect data like temperature, moisture level in air and surroundings which is made to blend with satellite data. They use this data in Variable Irrigation Rate technology which enables the farmers to conserve water using sprinklers which will turn water off over areas that don’t need water and turn them back on over areas that need water.

Fig: 1.1


Fig: 1.2


Fig: 1.2 shows the cloud water density, that is water content in the cloud which is important in figuring out which type of cloud is going to form and helps to determine the cloud formations that are likely to occur, which is extremely useful for weather forecasting

According to IBM, farmers will be able to track weather conditions in 10 min increments up to 72 hours in advance. And a full 72 hour forecast will create data around 320 gigabytes but while each individual farmers will require a small tranche of it in a personalized way. They are also building a weather model with 1.5 kilometer resolution for the farmers. It is estimated to save 15% of the total water that is been used in irrigation that is about some million gallons per year. The costing comes around $20 – $40 per acre for first 3 years.

 Fig: 1.3


With geospatial mapping, sensors and predictive analytics, farmers will be presented with real time data in time series, graphs at a granular level. Soil quality, field workability, details on nitrogen, pests and disease, precipitation, temperatures, and harvest projections with even predicting the expected amount of revenue in relation to the commodity’s market trend and all of this is been analyzed and reported via a smartphone ( Fig: 1.3 ), tablet, or desktop. In future it may even become mandatory to use IoT and Analytics in agriculture industry for sustaining and growth, in order to maximize its revenue in multiple folds with the minimal use of resources.


The IoT is on its way to becoming the next technological revolution with $6 trillion to be invested before 2020, and a predicted ROI of $13 trillion by 2025 (cumulative of 2020-2025). Given the massive amount of revenue and data that the IoT will generate, its impact will be felt across the entire big data universe, forcing companies to upgrade current tools and processes, and technology to evolve to accommodate this additional data volume and take advantage of the insights.




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