How predictive maintenance and analytics can improve farming

With a worsening climate crisis and conflicts affecting global supply chains, it’s probably safe to say modern agriculture faces an uncertain future. To survive, we need to evolve modern agriculture, making it more productive, sustainable, and efficient.

Agritech, the innovative use of information technology to improve farming yields, could prove vital. In general, we have seen the use of Artificial Intelligence (AI) and Robotics, Computer Vision, and the Internet of Things (IoT) to look at soil, weather patterns, crop fields, etc.

However, there is another area of farming where these technologies can make a difference – equipment repair and maintenance.

Maintenance – why it matters in agriculture

Recent years have witnessed a steady increase in annual production expenses in US agriculture – 12% in just one year from 2020 to 2021. And repair and maintenance costs have played a big part in that spike, going from $14.54 billion in 2018 to over $18.55 billion in 2021.

This is not a short-term trend – maintenance costs have increased by 16% since 2013. These are big numbers in an industry plagued by low-profit margins despite heavy government subsidies. Adjusting for inflation, both net farm income is projected to decrease by 7.9% in 2022.

Unexpected equipment breakdowns and emergency repairs can have a lethal financial impact, especially on smaller farms

In this scenario, unexpected equipment breakdowns and emergency repairs can have a lethal financial impact, especially on smaller farms. Frequent repairs can impact cash flow. Emergency repairs can often take days, resulting in critical productivity losses in certain seasons.

Increasing the frequency of scheduled maintenance can be counterproductive due to higher costs and disruptions. Predictive maintenance is the preferable option – using data analysis tools and sensors to detect any anomalies/potential faults in advance and estimate the optimal time for maintenance.

Corrosion and fatigue in farm equipment

Corrosion is the nemesis of farmers when it comes to farming machinery maintenance. Unlike in industrial maintenance, corrosion supersedes ordinary wear and tear due to the machines’ constant exposure to sunlight, the elements, and the chemicals in fertilizers.

It also means that with regular use, farming machinery and equipment require more maintenance than machines safely located inside factories. Timely maintenance schedules and availability of spare parts are essential to reduce the risk of accidents and breakdowns.

And farming equipment staying idle for weeks or months on end, usually during winters, doesn’t help. You need a proactive approach to equipment maintenance to keep things running smoothly on any farm, large or small.

Ways to implement predictive maintenance in farming

Since corrosion is a major concern, any attempt at predictive maintenance in agriculture will have to address it head-on. We have several promising pathways open to us:

GIS Systems

Geospatial Information Systems are already widely used for the spatial analysis of various factors like temperature and precipitation that affect cultivation and productivity levels in agriculture. Using relative humidity and temperature predictions we can identify specific phases during the year when corrosion risk is at its peak.

IoT Sensors

The oil and gas industry already uses IoT sensors attached to pipes, storage tanks, and other vulnerable assets to identify spots that are weakening or thinning due to corrosion. One popular technology uses changes in an object’s magnetic field to measure corrosion levels. Similar magnetic sensor technology could be used across warehouses and storage facilities on farms.

GPS/Activity Tracking

Predictive maintenance cannot ignore the normal wear and tear caused to engines. One simple way to keep track of such activity is using GPS-based systems or simple tracking sensors to measure things like total distance traveled, hours of usage, and so on.

Harnessing data through AI-powered CMMS

The data collected through these diverse pipelines can be used to create AI-powered models that predict the ideal time for predictive maintenance. To implement it, you need a computerized maintenance management system (CMMS).

The software allows farmers to create a centralized database where they can track and manage everything from maintenance frequency to spare parts inventory, labor efficiency, operating costs, and more.

In the past, only large corporations could afford sophisticated maintenance software. These days, thanks to the rise of software-as-a-service (SaaS), even smaller enterprises and farming operations can use an AI-powered CMMS to unlock the following benefits:

• Cut down maintenance costs by reducing the frequency of routine maintenance
• Reduce productivity losses due to unplanned downtime
• Cut repair costs by decreasing the frequency and severity of asset breakdowns

Big data is fast becoming the next big strategic input in agriculture. With proper analytics, structured data can be converted into actionable intelligence. However, we are still in the early stages of this smart transition. Through greater investment and focus on predictive maintenance solutions, we can make a positive financial impact on farmers.