In this episode, Jason talks to Natalia Connolly the Vice President of Data Science at Infinite Acres about Agricultural Tech (AgTech) and how it is helping the agriculture industry keep up with increasing demand
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Agriculture may not be the first industry that pops into your head when you think about data and technology but it is a global industry that affects the vast majority of us on a daily basis. With the assistance of technology and data the agricultural industry not just be improved but completely overhauled over time with more sustainable methods and tech.
[00:57] Natalia’s background in astrophysics and attaining her pHD to working in data
[02:29] Infinite Acres and how they are working to improve food production through vertical agriculture
[04:30] Why vertical AgTech has become possible now with recent advances in technology
[06:40] How data science assists the progression of AgTech
[09:16] The huge feedback loop that AgTech uses with data to improve operations
[10:53] How crops that use AgTech are different from crops grown normally
[14:30] The unusual data sets used in AgTech
[18:28] How data in AgTech is used beyond the growing of crops themselves further down the supply chain
[19:30] Where this type of tech is most needed
[21:05] Sustainability in AgTech
The idea of implementing technology in agriculture is definitely not new but until recently had not been available for commercial use. At the moment, most agricultural organisations and farmers rely on data to constantly innovate to improve their current operations by optimising resources, increasing efficiency and boosting yields. However the combination of data and tech means that there are new types of farms being created that leverage both from day one such as vertical farms which are the next step in agriculture. These farms are built indoors and use less land, water and energy than traditional farming methods due to technology and data to enable a carefully controlled environment.
Precise, controlled environments that are created with the combined use of data and tech mean that these farms are able to grow and propagate crops more predictably and economically than traditional farms who may be at the mercy of external factors. One key example is the impact of drought which can leave farmers without crops for months or even years. Instead of having to go without certain crops or leaving suppliers with no stock, AgTech and vertical farms can still achieve a quality crop with almost a 95-97% reduction in water usage.
Farmers have been growing crops for thousands of years and our current knowledge is only in existence due to the tireless work of many people who devoted their lives to working the land. There is a misconception that with the introduction of new technology people will become redundant in areas such as agriculture which has the potential to become highly automated. Instead, the use of technology and data only serves to enhance current knowledge and capabilities which has been thousands of years in the making. For example, in AgTech there is still a need for crop specialists who need to understand crops and plant biology just like traditional farmers. Instead of splitting up their time with many manual labour tasks they are able to fully focus on solving other problems and deepening their knowledge.
AgTech isn’t just about improving crops and growing more food for the consumer. There are also many other issues that surround this industry such as sustainability and the impact of farming on the planet. AgTech can help conserve precious resources and help to keep a predictable amount of crops growing despite external factors.
Some examples of how Vertical Farms help with sustainability include:
AgTech is a new and rapidly developing field where data is vital to its constant improvement and success. Vertical Farms show how large webs of data can span not just within the operations of the farm itself, but also the supply chain and beyond. This creates efficiency and predictability in an industry that is usually reliant on so many unpredictable external factors.