Recent technical advances such as the Internet of Things (IoT) have given farmers the power to dramatically change the way they work. With IoT, farmers add intelligence to analog and mechanical devices. They can streamline processes, gain efficiencies and build stronger businesses overall. Smart farming is the name for this new approach to agriculture and there are many examples across the industry.

Gathering information such as environmental conditions improves the quality and quantity of produce while minimizing risk and waste. The technology also adapts to specific machinery and systems, such as tractors. It uses the data collected to provide a complete real-time view of operations. Smart agriculture affects every aspect of the farming process. Smart farming solutions have gained increasing traction and acquisitions are on the rise. The global smart agriculture market reached $14.1 billion in 2021. It is expected to grow to $25.25 billion in 2027, exhibiting a CAGR of 9.8 percent. Here are five examples of how smart agriculture is changing farming.

Examples of Smart Agriculture

1: Land Management

In the agricultural industry, land is seen as the foundation of everything. Growing and harvesting crops fluctuates constantly. It therefore greatly affects a business. With IoT, farmers can do all of the following:

Learn about soil composition, rainfall and temperature to maximize soil performance.

Decide whether pesticides or fertilizers need to be added or removed.

Rely on irrigation sensors that can monitor soil dryness and operate sprinklers accordingly.

Agribusinesses gain real-time visibility into soil vigor and unique soil conditions to get the most out of the land. IoT gives farmers the ability to learn more about what’s happening so they can manage proactively, not reactively.

2: Product Monitoring

Farmers want to achieve consistent crop quality and avoid various deviations, and IoT helps with this. Sensors continuously monitor items such as leaf quality, color and root strength. Then compare current measurements with historical data and determine how well the crops are growing. With IoT, farmers can do all of the following:

Better predict the production flow.

See crop growth.

Watch out for any abnormalities such as diseases, pest infestations or harsh climate that will reduce yields.

Understand what their final output will be.

Set better expectations.

Improve product distribution.

Monitor operating expenses more accurately.

Know when to plan the next shipment of seeds and grain.

This way the business flows more consistently. Once the finished product is in distribution, the next batch is ready to be planted. Insights reduce production risks and empower farmers. So they don’t face crop shortages and income cuts.

3: Predictive Maintenance

Another critical smart agriculture example is predictive maintenance. The advent of smart IoT sensors enables suppliers to collect device performance information as equipment functions. AI, machine learning and data analytics measure the typical efficiency, wear and tear of an asset based on items such as vibration analysis, oil analysis and thermal imaging. Predictive models have algorithms that determine when an asset needs to be serviced or repaired. Benefits include:

Extended machine life.

Reduced downtime.

Increased employee productivity.

Data from the US Department of Energy shows that predictive maintenance is extremely cost-effective. Implementing a predictive maintenance program provides

A tenfold increase in ROI

25-30 percent reduction in maintenance costs

70-75 percent reduction in breakdowns.

35-45 percent reduction in downtime.

In essence, farmers get a much better way to keep their equipment running at peak performance.

4: Livestock Management

Traditional livestock monitoring methods relied on individuals manually inspecting animals and looking for signs of disease or injury, a costly, highly unreliable and inefficient method. IoT livestock management solutions take the guesswork out of determining an animal’s health. How does IoT livestock management work? Battery-powered sensors using a wearable collar or tag monitor an animal’s location, temperature, blood pressure and heart rate.

The information is sent wirelessly to an app in near real time. Farmers access the information through their mobile devices and are thus able to

Check the health and location of every animal in their herd from anywhere.

Get alerts if a metric falls outside the normal range.

Know immediately which animals are affected and which are not.

Plus, farmers no longer need to physically examine each animal’s vital organs to see if a disease is spreading. Temperature monitoring helps identify the peak of the mating season. Livestock monitoring solutions use tracking to collect and store historical data at preferred grazing points. Keeping livestock healthy is important. Because if they get sick, their development lags behind their cohort. Such animals typically fail to keep up with the rest of the herd and become less valuable to the farmer. With this example of smart agriculture, farmers are gaining more insight into the health and well-being of their animals.

5: Process Automation

Farmers need to increase their yields. Decades ago, farmers started replacing manual work with machines. Now IoT offers them the next step in this process:

Computer technology to take over jobs typically done by hired hands.

Streamline repetitive manual tasks such as watering, fertilizing, pest control and even planting seeds.

Review large volumes of performance data such as crop growth, herd feeding and soil conditions.

Find deviations.

Automatically send alerts to staff smartphones when needed.

With these capabilities, they become more knowledgeable and more proactive. They see problems, investigate the problem, troubleshoot, create workarounds and work faster and more efficiently.

A Competitive Industry

Farming is a mature, highly competitive and intensive industry. The smart farming examples above highlight this. Emerging IoT technology streamlines operations in areas such as soil management, predictive maintenance and automation.