How Do You Measure a Mountain of Sugar Beet? OWL EYE, LiDAR and Agricultural IoT
A pile of harvested beet is one of the least glamorous objects in agriculture. It is also a surprisingly good way to understand why the word “sensor” no longer means what it used to.
A German system called OWL EYE, built by Sachtleben Technology, uses LiDAR to scan a stockpile of sugar beet and turn it into a live three-dimensional model, measuring its volume to better than 98% accuracy, colour-coding the material by age and feeding the result into enterprise systems such as SAP. It is a clean example of a broader shift: the endpoint is no longer a device that returns a single reading. It is becoming an application in its own right, and that changes what the network around it has to do.
The problem: how much beet is in that pile, and how old is it?
Sugar is one of the largest agricultural processing sectors in the world, and demand keeps climbing, with global consumption projected to reach around 223.1 million tonnes by 2033. During the campaign, factories run around the clock to process an enormous harvest before the sugar content of the beet begins to fall.
Despite that scale, many sugar factories still manage their beet stocks with dated methods: manual estimates, rough input-and-output calculations, or occasional weighing. Those approaches produce inaccurate numbers, which in turn trigger unplanned production stops and costly last-minute logistics. There is a quality dimension too. Beet is not inert. The longer it sits in store, the more its sugar content drops, so a factory needs to know not only how much it has but which parts of a pile should be processed first. Without visibility, that is nearly impossible to judge.
What LiDAR actually does to a stockpile
LiDAR (light detection and ranging) works by firing rapid laser pulses at a surface and timing how long each reflection takes to return. Because light travels at a known speed, each timed reflection becomes a precise distance measurement. Sweep those pulses across a whole beet pile and you collect millions of individual distance points: a point cloud that describes the shape of the heap in three dimensions.
Software then turns that point cloud into usable geometry. It reconstructs the surface of the pile, references it against the known ground beneath, and calculates the volume enclosed. Scan again a few hours later and you can see exactly how the volume has changed as beet arrives and is drawn down. This is the step that matters: a measurement that used to be a guess becomes a continuously updated number. Sachtleben’s OWL EYE system applies exactly this approach, and the same technology is used to measure throughput on conveyor belts with a scanner mounted above the belt as well as static stockpiles in yards and silos.
Beet storage is dirty. Dust and debris would quickly blind an optical sensor, so the LiDAR unit’s lens is cleaned automatically with compressed air, and the unit is given a stable power supply for continuous operation. It is a small detail that captures how different field IoT is from a neat sensor on an office desk.
From a measurement to an inventory
A volume figure on its own is useful. Turning it into a decision-making tool is what makes OWL EYE interesting. The system provides a real-time 3D visualisation of the outdoor store, colour-codes the beet volume by age so operators can see which material has been sitting longest, and feeds that inventory data directly into enterprise systems including SAP. Stock management shifts from estimation to a reliable, data-driven process: the factory can see how much it holds, where it is, how old it is, and what to process next.
That is a meaningful change in how a physical commodity is handled. The beet has not changed. What has changed is the ability to observe it continuously and act on what is seen. That is the whole story of agricultural IoT in miniature, and it is why we treat this deployment as the way into our wider Teltonika in agriculture coverage.
Why the connectivity is not an afterthought
Precision alone is not enough. To be useful, the monitoring system has to exchange data continuously with the wider ecosystem of enterprise software, logistics tools and quality systems. That calls for connectivity that is secure, stays up, and can move large volumes of data in a demanding industrial environment.
In the documented sugar deployment, all of the LiDAR sensors and controllers connect to a Teltonika TSW210 industrial Ethernet switch, which consolidates the many data streams into one stable flow across its eight Gigabit ports and two SFP ports while minimising packet loss. From the switch, the data reaches a Teltonika RUTX50 5G router, which splits it onto two paths: one to the enterprise server for storage, processing and integration, and one secured over a VPN for real-time remote monitoring of the LiDAR data. With throughput up to 3.3 Gbps the router can carry even the largest LiDAR datasets live, dual SIM with auto-failover keeps the link up if a network drops, and support for MQTT, Modbus and SNMP lets the data feed straight into SCADA systems, IoT platforms and cloud analytics.
The router is not the application. OWL EYE is the application. The LiDAR measures, local software builds the model, the switch consolidates the traffic, and the router provides the secure bridge into the wider organisation. Change the industry and the shape stays the same.
The real lesson: the endpoint is becoming an application
Ask most people to picture an IoT sensor and they think of something that returns a single value. A temperature probe reports 18.7°C. A tank sensor reports a level. A door contact reports open or closed. Cheap, simple, one number.
OWL EYE does not fit that picture at all. It does not return a reading. It produces a digital representation of a physical environment: a live 3D model of thousands of tonnes of material, complete with age and volume. A smart camera classifying crops, a GNSS unit calculating centimetre-accurate position, an edge-AI box interpreting a video feed, these are the same phenomenon. The endpoint has stopped being a thing that transmits a measurement and started being an application that runs at the edge.
That has direct consequences for the network. Instead of connecting thousands of trivial sensors individually, agricultural and industrial systems increasingly contain local clusters of capable equipment behind an intelligent gateway that aggregates, processes and forwards only what matters. It is the same convergence we track across hardware in smart module vs SBC vs router: the line between sensor, gateway, edge computer and router keeps blurring, and agriculture is one of the clearest places to watch it happen.
Not just sugar beet
Sugar beet is a memorable example, but the approach is not specific to it. The same LiDAR inventory technology is used in mining, recycling and construction to monitor stockpiles and material flows, and the principle carries across to grain stores, aggregates, biomass and any bulk material whose quantity, location and age a business needs to know in real time. Wherever a heap of something is worth money, being able to measure it continuously turns it from a guess into a managed asset. The same connectivity questions we cover in SCADA over cellular then apply: what protocol, what resilience, and where the data needs to end up.
Frequently asked questions
What is OWL EYE?
OWL EYE is a monitoring system from Sachtleben Technology that uses LiDAR to measure the volume of bulk material such as sugar beet in outdoor yards, storage boxes or silos. It builds a real-time 3D model with a claimed accuracy of more than 98%, colour-codes the material by age, and feeds inventory data into enterprise systems including SAP.
How does LiDAR measure a stockpile?
A LiDAR sensor fires rapid laser pulses at the surface and times each reflection to calculate distance, building a point cloud of millions of measured points. Software reconstructs the surface, references it against the ground beneath and calculates the enclosed volume. Repeat scans track how the volume changes as material arrives and is drawn down.
Why does stored sugar beet need monitoring?
Beet quality falls in storage because its sugar content drops over time. A factory therefore needs to know not just how much it holds but how old each part of the pile is, so it can process the oldest material first, avoid quality losses and reduce unplanned production stops and last-minute logistics.
What connects a system like OWL EYE to enterprise software?
In the documented deployment a Teltonika TSW210 switch aggregates the LiDAR and controller data and a RUTX50 5G router carries it, splitting it between the enterprise server and a secure VPN for real-time remote monitoring. Support for MQTT, Modbus and SNMP lets the data feed into SCADA systems, IoT platforms and cloud analytics.
Is LiDAR inventory only used for sugar?
No. The same approach is used in mining, recycling and construction to monitor stockpiles and material flows, and it applies to bulk materials generally, from grain and aggregates to biomass. Any store of bulk material whose quantity, location and age matter can be measured this way.
