Predictive Maintenance in Farming and Agriculture

Predictive maintenance is one of the most sought-after maintenance strategies available today. High-tech features in futuristic movies are now becoming reality as the technology becomes available. People and devices are connecting in unprecedented ways that allow data to flow freely. In this article, we’ll see how one of the most advanced forms of maintenance applies to a rather traditional industry such as farming & agriculture.

What Is Predictive Maintenance? 

Predictive maintenance, commonly referred to as PdM, is one of the more technologically advanced maintenance strategies available. Instead of performing maintenance tasks according to a time-based or usage-based schedule, PdM determines maintenance requirements according to the condition of the asset. This allows more focus on performing tasks as needed.

Traditional schedule-based maintenance poses the risk of performing too much or too little work. On the other hand, reactive maintenance endangers not only the life of your equipment. but also your overall operations and safety. PdM strikes a balance between these two ends of the spectrum.

How Predictive Maintenance Works

PdM works by installing sensors and measuring devices on key assets. These devices collect data about the condition and overall performance of the selected equipment or facility. When these sensors pick up a certain set of information, a maintenance work order is automatically triggered. In a traditional plant, for example, vibration analysis can detect unusual levels of vibration on rotating equipment. This then calls the attention of maintenance personnel to inspect and service the machine as needed.

With advancements in communications and technology, PdM has gained the capacity to live up to its name in being a predictive tool. Applications of predictive analytics are expanding from a few select assets to virtually any aspect of an industry. With all this data, companies become well-equipped to learn the conditions that would prevent problems and increase productivity. This is all made possible by allowing your whole facility to be virtually linked together through the internet of things (IoT).

How Can Predictive Maintenance and Analytics Improve Farming & Agriculture?

One of the factors that sets predictive maintenance apart is its capacity to obtain and analyze massive amounts of data. The analysis of this information is the backbone of all implementation decisions and strategies.

From an agricultural point of view, analytics are not only limited to machines and assets. The data covers the overall conditions of the farm or the larger facility. Here are a few ways data analytics are revolutionizing agricultural methods:

Boosting Productivity With Data

Global food demand is projected to reach exceptionally high levels – looking to increase anywhere between 59% and 98% by 2050. To keep up with demand, food crop growers are using all the data they can get to maximize productivity.

In a technique known as precision agriculture, farmers install soil probes and sensors throughout the farm. These data points are used alongside external data from local weather channels and collective advice from other farmers. Using tractors equipped with GPS, farmers can move about the farm with precise information about its specific zones. With all the data available, they can allocate time and resources such as fertilizers and water more efficiently. A study shows that this technique can streamline the usage of resources while increasing production by approximately 30%.

Opportunities for Cost Savings

As the previous point mentioned, data analytics and predictive maintenance allow for more efficient allocation of resources. Instead of spending the same amount of nutrients and resources across the entire farm, practices such as precision farming allow for a more targeted approach.

While these practices usually require an initial investment, studies show that the returns are promising. By developing analytics on precision farming alone, there’s an estimated $250 billion worth of savings per year to be earned across the globe. Considering other methods than precision farming, worldwide annual savings are estimated to be in the trillions just in reducing waste and increasing productivity.

Improved Supply Chain Management

The agricultural process doesn't end at producing goods. As with any other output-based industry, the supply chain is essential in getting the product to the consumer. Predictive analytics, even data analytics in general, applies naturally to the management of supply and demand.

Improving the supply chain is already a known advantage of using modern CMMS software. For instance, maintenance tasks in work orders are linked to cataloged components and their warehouse inventory data. Any parts that are not available on hand can be set up to automatically generate purchases to fulfill demand. Imagine expanding that capability with more intelligence working in the background to tell you what you need and by when. The added visibility on data allows farmers to connect with retailers, and suppliers to get their resources more efficiently.

Environmentally Sustainable Operations

Arguably one of the most important reasons to consider moving toward a predictive strategy is the opportunity to operate more sustainably. There has been a big shift in recent times to consciously eliminate, or at least reduce, environmentally harmful practices. And for good reason, climate change and environmental deterioration pose serious threats to agriculture. The good news is that a predictive approach allows you to operate more sustainably, while also achieving more efficient workplace practices.

How Can Predictive Maintenance Make Farming & Agriculture More Sustainable?

Before appreciating the solution that a predictive strategy offers, it first helps to take a step back and understand the problem. Nitrogen is a major component used in agriculture to promote higher yield in crops. Because nitrogen is relatively cheap, it becomes prone to being overused. It starts to become dangerous when the excess nitrogen has grown substantially. An overabundance in nitrogen can cause severe damage to the environment. The biodiversity in immediate areas are impacted, bodies of water are harmed, and drinking water for people and livestock can potentially be contaminated.

Adding in too much nitrogen can be avoided by developing a systematic method for determining safe and reasonable amounts. This can be done by practicing sound methods of gathering and analyzing relevant data. Here are a few ways a predictive approach helps reduce nitrogen usage to avert the environmental consequences of agriculture:

Understanding the Production Process

Farming involves a lot of variables. These include ever-changing factors such as weather and shifts in climate periods. By identifying these variables and setting up ways to measure and predict them, you can gain a better understanding of the whole process.

Making Data-Driven Decisions

After establishing a clear understanding of the process, you can interpret the information to come up with data-driven decisions. Some key factors to think about are plant and harvest timing and schedules, cost and investment preparations, and yield projections.

Building Data

The steps we've covered so far become increasingly effective as you collect more information. Having broader historical data provides you the opportunity to have a more solid basis for your predictions. Using a predictive approach allows you to build your data in several ways. For example, you can build upon publicly available historical data on soil characteristics, weather, and climate patterns. In addition, you can build your dataset by installing sensors and monitoring devices.

Generating Insights for Decisions

Actionable steps start out from logical insights. Based on all the data-gathering and analysis, you should be able to come up with corresponding recommendations of proper nitrogen levels. This allows you to avoid overusing nitrogen-rich materials to blindly hope for higher yields. Instead, you can optimize when to start using these nutrients and how much to use.

Improving Existing Processes

Expect each season to be different in one way or another. Take each cycle as a learning opportunity by properly documenting your experiences. You can make the most of each run by highlighting actions that were done well and identifying steps that need improvement. The whole process is intended to be cumulative. It looks to improve processes for future seasons.

Conclusion

Predictive analytics, combined with big data analysis and artificial intelligence, are pushing the boundaries of progress further every day. While some external factors in agriculture remain uncontrollable, they're no longer unforeseeable. Factors that used to be left to chance are now understood better with precise visibility. Vital data to manage crop production can now be made continually available. For example, pest patterns and simulations, soil quality information, and crop yield projections can be gathered and calculated using a system of data collection.