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This grower discovered irrigation issues with just 3 sensors

August 16, 2018

It may go against “sales logic,” but we usually advise customers to start with small deployments.

It’s not because we can’t handle large ones (one gateway can support around 4 thousand sensors– and if more are needed, it just takes another gateway) but because working with crop-level insights is a process. It becomes a part of how growers, consultants, irrigation specialists, cold store managers, etc. make decisions, plan resources, and communicate with one another.

There’s no magic formula to how many sensors a grower of X crop needs in a Y by Z sized facility. Growers have varying objectives, differing circumstances, and let’s be honest, differing budgets.

A quick scan of the internet would likely lead anyone to conclude that they need thousands upon thousands of data points– big data! huge data!– to make any meaningful decisions for their agribusiness.

Au contraire.

Of course, many data points are great. The more data at your disposal, the more likely you are to notice something you weren’t even looking for: an opportunity to cut costs, conserve resources, prevent crop damage etc.

But if you’re strategic with your priorities, you can make a difference with much, much less.

Go for the low-hanging fruit

Forgive the pun, but we go on about this concept for a reason. Real-time monitoring can deliver quick wins in the short-term, while helping shape growers’ strategy across seasons.

The above image is a customer’s heatmap in the ZENSIE dashboard. With just three VWC sensors, this grower was able to identify and respond to an irrigation issue, quickly and efficiently.

Faulty irrigation can be costly to growers– water is expensive (not to mention Europe has been experiencing drought!), and the impact on plants can be damaging.

Easily visible changes (all with optional SMS or email alerts) in moisture levels quickly showed the customer than an irrigation pipe was overwatering on one end, while underwatering on another. The uneven distribution of water was due to a blockage– quickly remedied thanks to real-time data.

Data doesn’t have to be “big” to be powerful.

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