Automating agriculture workflows with Mapify

Updated: Sep 12


Let's face it, most business operations require some sort of location intelligence and as much automation as possible for repetitive tasks. Yet, these two seemingly simple concepts usually demand deep technical expertise, lots of coding, and some degree of infrastructure management.


This is where Mapify really shines! Mapify Workflows combines a drag-and-drop approach to creating workflows along with Mapify's powerful location intelligence capabilities.


Simple geo-fencing workflow example

Simple geo-fencing workflow example


This means that you can quickly implement your business rules without any coding, and it will all run completely scalable on the cloud. You don't have to worry about setting up or managing any infrastructure, you can simply focus on growing your business. Mapify will keep up.


Many customers look for geo-fencing solutions, for example. Their approach usually starts with checking if the location of a vehicle/device/sensor/event is inside or outside a predefined set of geographic regions. Detecting if a vehicle entered a restricted area, if a specific security tracking device just left an authorized area, or simply detecting which tariff zone a bus is traveling on are a few examples.


For Mapify, this usually means creating a workflow which handles incoming messages. You have to add a workflow node to intersect the location data from the message with a zones layer and select the desired action to be triggered. Yes, it's that simple!


With our software, you can pretty much build quick a geo-fencing solution in 5 minutes with just a few clicks. Just like that, you have a production-ready scalable and automated process doing all the heavy lifting for you. Mapify Workflows and geo-fencing are a perfect match, but there's so much more... keep reading.


The AgriTech challenge


One of the most interesting use cases for Mapify Workflows has been helping a large international food processing company optimize its operations while reducing its carbon footprint and food waste.


This company harvests tomatoes from hundreds of tomato fields and loads them up into large truck-based containers. After that, these trucks move towards one of the processing plants, taking into account the type of tomato loaded and each processing plant's real-time needs.


Some of the major pain points for this client were the need to track in real-time the status and location of hundreds of truck-based tomato containers and manage their routes and destination factories.


The client needed to:

  • Determine container locations

  • Determine when a container is in the harvesting process (loading)

  • Determine if a container is loaded or is empty

  • Determine the type of tomato loaded into a container

  • Determine if the container is on route to a tomato field to load or to a processing plant to unload

We had some limitations, such as only being sent NMEA based GPS messages (there were no additional sensors in the containers) from tracking devices in each container.


Mapify to the rescue

The first step consisted of geo-referencing all the tomato fields in a Mapify polygon layer, making sure each field included attributes for its owner (most fields belonged to third-party tomato suppliers), tomato type and some additional data.


After that, all processing plants were also geo-referenced in a Mapify polygon layer, to make sure truck weighting access areas and truck parking areas were included.


Tomato fields and processing plants were geo-referenced using Mapify Editor

Tomato fields and processing plants geo-referenced using Mapify


A points layer was then created to hold the latest location of each container, which would be updated in real-time as the GPS messages were sent to Mapify by the container tracking devices. Each container feature would include its identifier and location, but also its load status (empty/loaded), type of tomato loaded (when not empty), current speed and timestamp of the last received location message.


After creating a data feed in Mapify to handle the incoming messages from the tracking devices, it was a matter of creating the first workflow to process those messages.


This workflow allowed not only to update each container position in real-time, but also to determine its load status and type of tomato loaded. This was achieved by using layer intersection conditional nodes which could determine if the container was on a field, on a processing plant, or on a route.


Illustrative example of a workflow which updates each container with its correct status and location

An illustrative example of a workflow that updates each container with its correct status and location


All containers started the day with their load status as empty, but when a container was in a field, attribute-based conditional nodes determined if the container had finished the harvesting operation, after which its status was considered loaded.


The tomato type loaded onto the container was defined by the type of tomato associated with the field container during the harvesting. When a container was not in a field or in a processing plant, it was on route. If its load status was empty, it was on route to a field. Otherwise, it was on route to a processing plant.


When in a processing plant, attribute-based conditional nodes and a simple integration with the client weighting platform determined if the container had already been assigned a cargo weight (this meant it went through the unloading process) in which case its status was considered empty.


Monitoring workflow performance with a dashboard


Each Mapify Workflow has its own performance monitoring dashboard

Each Mapify Workflow has its own performance monitoring dashboard


Another great feature of Mapify is that you easily can keep an eye on your workflow usage and performance through a monitoring dashboard.


Results after implementing Mapify Workflows


Mapify provided a real-time view of all operations. There were real-time alerts for specific outlier events and generated accurate daily reporting of all operations. This resulted in an increase in operational efficiency by 20%, and waste reduction by 10%.


🤔 "How could this client further improve their operations?


Tomato fields could be tracked regularly to check each field's tomato quantity, status and maturity. This would allow truck routes and harvesting to be optimized.


Or Mapify integration features could be used to stream operational data to a data lake such as Google Big Query for more complex analysis and building prediction models using machine learning.


🤔 "Are there any other use cases for Mapify Workflows?"


Well, any business that needs to handle incoming messages and automate a correspondent action can benefit from using Mapify Workflows.


Maybe you're building a micromobility solution for tracking the usage of your fleet of electric scooters and optimising your daily operations. Maybe you're a modern fintech startup looking to easily add credit card usage validation based on geographic location to your app as an additional measure of the security process. You may even be looking into creating a geo-marketing platform to serve campaigns targeting potential customers who are close to your store locations.


Wondering if your IoT workflow works with Mapify? Try us 👉 mapify.ai/trial.