Agricultural decisions are increasingly informed by data that can’t be captured using visual inspection.
What the field looks like might be healthy, but the soil moisture might be fluctuating, water levels might be changing, and environmental factors might put stress on the area. Sensor equipment, lab analyzes, GIS and data-logging tools can convert this into actionable information though.
The interesting thing is not any of these tools singularly, but rather how they integrate in order to collect and analyze a variety of data.
Going from Field Conditions To Data
A basic agricultural monitoring system can be thought of as something like this:
Environment → (Sensors) → Data collection → (Data analysis) → Decision
Various kinds of sensors can collect specific numbers relating to soil-moisture, temperatures, or any other kind of measurement. They can be received by a data logger or transmitted to another tool which stores and analyzes them.
When it comes to actually analyzing the information at a larger scale, it becomes useful to compare and analyze it beyond just raw values of measurements.
The Importance of Getting Soil Info Right
Soil is never uniform and its conditions can vary from place to place, even from fields to fields.
Lab soil analysis can offer information about the pH levels and qualities of the soil, whereas sensors can read more frequent fluctuations in the moisture levels. The two are able to answer slightly different questions.
Lab soil analysis can provide answers in a more in depth way for a given spot, meanwhile, a sensor can demonstrate a value in a given point of time.
Pairing them can allow a deeper analysis that’s informed by the other.
The Role of Water Data
Water presents a whole layer of environmental data that is necessary to think about.
IRRIGATION is dependent on this, but water tends to come with salinity and other qualities that may need to be tested.
Having water testing equipment can allow analysis that pairs this with soil to see any irrigation-related trends.
It’s a good demonstration of how this information grows more valuable when placed in context with other elements.
What GIS Has To Do With It
The ability to make sense of this data often involves a large-scale analysis of it, with GIS being one of the tools that can help.
Instead of hundreds or thousands of datapoints from sensors all over, having them organized so it's possible compare the readings from one location to others and see patterns or irregularities helps immensely.
It’s not just about the information itself, but what it signifies on a given place.
For instance, using a spatial database allows people to see if one region tends to have significantly higher dryness than another one at certain times of the year.
What A Monitoring Architecture Looks Like (In Broad Terms)
Sensors - collect the environment data
The data logger/gateway - receives all data and keeps it in a database
The database - keeps track of historic logs of this information
The processing layer - has tools that clean this information and run it through analytical models
GIS or dashboard - gets this information from the database and displays it
Actual people using the system - interpret this information and use it to make decisions based on it
Depending on the scale, frequency and other elements the system will change, but it typically follows similar principles. More often than not, it's not about just collecting data indiscriminately, but rather making sure it's properly used.
Knowing How Important Historical Data Is
While the ability to make decisions based on current readings is great, storing older ones for later review and additional data points is invaluable.
If you have a certain field's moisture data logged during certain months but not all of them, it doesn't tell you much. Meanwhile, with a complete log, you'd be able to better predict what a future reading might say.
This is a part of why just having the ability to collect data is more important than you think, it's not just about the raw number but rather putting it in the right context for additional analysis later down.
How Useful Your Readings Are Depends On The Tools You Have
While data is great and helpful, systems that collect it don't replace years of professional expertise. Information has value, but understanding it in context of how it works and what other factors may influence it is much more helpful.
The real strength in this approach is thus not in just blindly logging everything with every available tool, but rather to learn the limitations and make it count.
The Future Of Agriculture Is About Getting Better At Working With Sensors, GIS, GPS, Lab Analysis And Other Tools And Technologies That Aid The Agricultural Sector In Making Better Data Informed Decisions
At this point, there's not going to be an easier alternative to getting a steady stream of high-quality measurements and readings. The push is not towards getting more information but analyzing it and making it into something that can help the agricultural sector. Armed with these tools, the best course of action would be to learn how to maximize each one's strengths to get an accurate model that reflects the real world.
Top comments (0)