Part 2 — Digital Agriculture

Connected Digitized Agriculture (Photo Credits [1])

Today, when we talk about the word technology, we associate it with 2 important terms — “Connected” and “Data”. Connected is referred to a device or a machine that can communicate with another device or a machine. For example, a small sensor on the agriculture field that can record the humidity and transmit the value to a computer on the farm or a remote server. Here, the humidity information that is recorded and transmitted is called the Data or simply call it as information.

When we start using these connected devices and collect the information (data) about various activities on the field and off the field, for the betterment of agriculture practice we call it Digital Agriculture.

Digital = Connected + Data

Digital Agriculture enables a powerful set of transformations in the farming practices by taking the data-driven approach over intuitions. One such example case study is PepsiCo successfully reducing water input to their potato crop by 26% over the past ten years [2]. One way they have done this is through locating sources of waste water for re-use in irrigation. They also monitor soil moisture, link this to weather forecasts and set more efficient irrigation levels. This can improve sustainability and water availability in countries at risk from drought.

Importance of Digital Agriculture (Photo Credits [2]

Every future technological revolution in the field of agriculture will be dependant on the data collected today!

Data or to simply say information is everywhere. For example, season, month, year, the crop that was grown is an information that has happened and that we know. Going a bit further, we also know about the harvest of that crop: did it go well, did it get affected by a disease leading to a chemical treatment and much such vital information in the agricultural practice are known to all our farmers and other on the field institutions. However, making this information available in a format that can be shared, analyzed, stored and use for the benefit of our farmers requires Digitization i.e., connected devices that can gather data, communicate, save and analyze. The crucial success in the digital agriculture requires us to answer the following 3 important questions:

• Are we capturing all the information/data that can be captured?
• Are we capturing these information/data in a format that can be used?
• Are we using or analyzing or revisiting these information/data?

Components of Digital Agriculture

Following are the 4 main components of Digital Agriculture:

1. Prescriptive Agriculture
2. Precision Agriculture
3. Enterprise Agriculture
4. Big Data in Agriculture

Digital Agriculture (Photo Credits [3])

Prescriptive Agriculture

As the name suggests, best practices and actions to take are prescribed or recommended. Mainly, these recommendations/prescriptions are data-driven. These data not only includes physical on the field parameters like soil nutrients, weather etc.. but also considers market demand and supply, geographical diversity, governmental policies, logistics and more such off the field parameters that greatly influences the actions in agriculture.

Precision Agriculture

Like we discussed in the previous post, precision agriculture is a farming methodology of using technology and taking data-driven actions.

Enterprise Agriculture

As we see, business entities are increasingly involving in agriculture for food production. Enterprise agriculture is a set of techniques that an enterprise takes to maximize the benefits in the agricultural practices. These entities use various data analytics and ERP (Enterprise Resource Planning) tools for modeling or simulating an action to a reaction. For example, simulating (modeling) various actions such as crop rotation, adding another tractor, and observing likely after effect.

Data in Agriculture

Precision and Prescriptive agriculture components are completely dependent on the data. Every future technological revolution in the field of agriculture will be dependant on the data collected today.

Aggregated data is more powerful than just about a piece of land.

There are various types of data in agriculture:

1. Sensor data: Humidity, leaf wetness.
2. Agronomic: Yield, as-applied, as-planted.
3. Machine: Engine parameters, Tractor status variables.
4. Public data: Satellite imagery, weather.
5. Business: Logistics, supply/demand.

and many more….

Storage cost and compute cost is almost negligible compared to the opportunity lost cost due to missing data. As we are still in the nascent stage of successfully applying the data driven technologies in the field of agriculture, we do not fully understand the importance of various parameters. It is easy to exclude the usage of a piece information than to create it over a long period of time. However, as the data collection is rapidly increasing, we need to now in parallel solve — Data quality and Data security. Data quality is making sure the data collected truly signifies an actual event on the field. Data security is making sure the data collected is only used by an intended authority for an intended purpose.

Collecting more data is almost never a problem. Having large set of past data is a game changer in the agriculture.

Barriers for Digital Agriculture

As with every technology, we need to address many barriers for mass adoption of digital agriculture.

1. Data Intellectual Property (IP): We are targetting to increasingly collect more data and is being shared. How do we manage the anonymity, who owns these data?
2. Security: The more connected devices we have more the data and more the control. However, this also means there is an increased risk of breach and malicious attack on such systems.
3. Employment: Agriculture has maximum employment in the world. With the increasing use of the technology, there will likely be a major displacement of manual labors. Are we ready to address these socio-economic challenges?

Future of the Ag Tech Companies

1. Native: Ag Tech companies need to increasingly focus on building the native solution. Nativity is not just in the language. Building an application that can run smoothly on a low bandwidth in a poorly connected area is a native solution for that area. Using a specific font size in the application based on the type of user being targetted is another extreme example of nativity in the solution.
2. Data rules: Every company should start moving towards being a data strategic company i.e., every solution built should focus on capturing all the flowing information. Not all the data make sense in a day, a week or a month, however, years of data gathered across hundreds of parameters will equip the companies to build more sophisticated and accurate solutions.
3. A more personalized product or service: Digital agriculture technologies may create demand for personalised services where products are tailored to the requirements of individual farms.
4. A closed loop: Digital agriculture technologies will enable a reduction in chemical usage, a reduction in waste and spoilage, and better matching of demand to supply
5. A collaborative ecosystem: Digital agriculture will enable better collaboration across the food supply chain. It will also create opportunities for providers of digital agriculture products and services to the industry.

Finally, if you are interested in Smart Farming — Do checkout — Fasal — Grow More Grow Better — https://fasal.co/. We are revolutionizing farming in India!

Image credits [3]

Follow me on Twitter: @skm4ml LinkedIn: @Sandeep Krishnamurthy

References

1. Alabama University Precision Agriculture Course — https://campus.extension.org/course/view.php?id=975
2. http://breakthrough.unglobalcompact.org/disruptive-technologies/digital-agriculture/
3. https://fasal.co/