Unlocking the Future of Agriculture with AI: The Role of Data Analytics and Machine Learning

Right now, everywhere you look in the Ag sector, there are conversations taking place around the way AI can help revolutionize farming. This certainly isn’t a new concept, as the concept of AI has been around a very long time. It recently picked up steam thanks to ChatGPT being released and the industry being set ablaze by the concept of using technologies like “large language models” to shape how farming practices are carried out globally.

The truth is, though, that these technologies take a good deal of time to go from a new concept to something ready to be put into production and really help augment the work taking place each day on the farm. Several technologies are rightfully adopted at a slow pace because new tools that don’t work right or as expected can have a significant impact on time-sensitive tasks throughout the year, or require extensive training to learn and/or adopt which can be expensive to learn and costly to implement.

Regardless of where you’re at on your technology adoption timeline when it comes to AI, there’s no ignoring the steady beat of progress across all areas of AgTech. From automated machinery to regulatory reporting solutions, AI is working its way into more technologies that are looking to leverage the advanced intelligence that AI solutions have to offer to help make it easier and more effective to leverage hardware and software solutions on the farm.

Three areas in particular where AI is making progress are predictive analytics, crop and soil health monitoring, and yield optimization.

Predictive Analytics: For the longest time, farmers have been looking at ways to get better methods in place to predict yields several months beforehand. From bud counting on apple trees to using statistics to predict potato yields without having to sample and dig, along with methods for crop load management throughout the year to get to targeted outputs, there are several methods farmers use today to know what they are getting when harvest comes around.

AI is allowing for more intelligent methods for prediction, though, by allowing farms to upload past and current data from across the farm (IoT, scouting reports, lab tests, etc) and then form an understanding of the numerous parameters that contribute to mature crops and identify pre-cursor variables as the year progresses that will have a greater or lesser impact to the overall outcome. At CropTrak, we use these tools to analyze the data from our customers over several years to help identify the best methods for determining quality and help provide “early warning indicators” to surface problems in fields before it’s time to harvest. ?

Crop and Soil Health Monitoring: One area where AI has grown the most is taking in data from scanning orchards and fields and using Machine learning-based “classification models” to detect conditions, measure crop growth, and help detect conditions for soil, leveraging data from soil sensors.

In addition to that, scouting applications have long been a stalwart for many farms looking to migrate from paper and pencil-based data collection to capturing data on mobile phones in a field, from pictures to manual text entry, with AI-based models sitting on the back end of these databases to help analyze and sort through the data being gathered.

This has been an emerging space for CropTrak, as we’ve been helping farms collect and manage their data for the past fifteen years and building robust reporting to help consolidate, manage, and identify areas of focus across automated and manual data collection.

As we dive further into the data alongside our customers, we are finding new and interesting ways to analyze and surface areas of concern for our customers that can avoid lengthy and/or costly mitigation steps by identifying problems early on and putting the right resources in place to handle whatever issues are facing the farm.

We’ve also begun to support broader relationships with companies in the IoT sensor space to further expand on the value we bring with our ability to ingest and store many different kinds of data, all tied together around common asset types for field identification and management. This allows us to leverage AI even more effectively, as data cleanliness and consistency are a significant precursor to getting the most from AI-based modeling methods.

Yield Optimization: Outside of forecasting and automated detection, having best practices for farming every step of the way being calibrated based on external variables such as adverse weather is a growing area for AI-based modeling. Every farmer who’s worked with the land for any length of time knows what ranges to look for when it comes to growing degree days, crop growth/health, or soil levels. With greater variability, though, such as adverse weather or shifting patterns for pests, comes the need to calibrate yield optimization throughout the year in different ways depending on the event that’s occurring.

More farms are leveraging AI-based methods to analyze and interpret farm data and provide prescriptive decision-making to augment farming operations to calibrate and adjust farming practices to optimize their yields. This could be a freezing event in May for an apple orchard or a significant rain event in August for a wheat field, or an unexpected increase in fertilizer costs given a geopolitical event. Expecting the unexpected is becoming more common and leveraging AI-based tools to help determine steps to take, and balancing those steps with the impact/cost is the next frontier for crop load management.

At CropTrak, we talk a lot about having good data collection practices throughout the year being the best hedge against unexpected events, as it can sometimes take a long time to gather enough meaningful data to help make better and faster decisions. Oftentimes, when the unexpected hits, it’s too late at that point to begin collecting and analyzing farm data. We work with farms of all size to build useful data collection tools, with data that can be leveraged for AI-based models for the scenarios we are describing.

Regardless of where or how you choose to use AI technology in your farming operation, the technology will no doubt continue to grow and expand across the AgTech sector and work its way into more and more use cases for farms globally. Being proactive about understanding how it works and how to leverage it means you’ll be ready for the right opportunity and do the right work ahead of time to maximize the benefits AI can bring to a farm.

Please reach out and let us know if you want to discuss this topic in greater detail or have any questions about how we are leveraging AI across the farming ecosystem.

More information at www.croptrak.com