Reinventing How Farming Equipment Is Remotely Controlled and Tracked

The agriculture industry doesn't typically come to mind when thinking about sectors on the cutting edge. Yet it has been forging ahead through each industrial revolution, and today innovative farmers are starting to incorporate high-tech solutions to address the new challenges the industry is facing.

Automation and robotics, livestock technology, artificial intelligence, and precision agriculture are all opportunities to harness the power of future tech to bring farming into the 21st century.

Farmers face similar challenges regardless of the time period, including inflating operating costs and a shortage of laborers. This is compounded by the world population continuing to rise, increasing the demand on agriculture.

Now, in the Fourth Industrial Revolution, the Internet of Things (IoT) offers an opportunity for the agricultural industry to maximize efficiency while reducing costs, directly addressing the demands and challenges.

What Is Smart Agriculture?

Smart agriculture is the use of IoT in agriculture. IoT sensors collect environmental and machine metrics to inform decisions, monitor the state of crops, and optimize efficiency. For example, smart agriculture sensors can track how crops are doing and help farmers use fertilizers and pesticides judiciously.

Overall, the agricultural industry hasn't adopted IoT as quickly as other industries or consumers have, but the market is quite dynamic. The disruptions of the pandemic, including the shortage of workers and supply chain issues, have pushed more farmers to adopt and innovate.

According to reports, the global smart agriculture market size is expected to triple by 2025, reaching $15.3 billion, compared with around $5 billion in 2016.

The market is still developing, but currently IoT can improve agriculture by:

IoT Use Cases in Agriculture

As the industry adopts more IoT devices, the use cases will likely increase. But here are the primary use cases for IoT in agriculture.

Precision agriculture: Precision agriculture offers information to make accurate, data-driven decisions and make operations more efficient. With IoT sensors, farmers can collect virtually any metric that's important for decision-making, including data on temperature, humidity, soil condition, lighting, CO2 levels, pest infestations, and disease. With this data, farmers can estimate the ideal amount of resources to use — like water, pesticides, or fertilizer — to reduce expenses and raise healthier crops and livestock.

Climate monitoring: Climate monitoring devices, such as weather stations with sensors, can be used to collect data about the environment. Farmers can then map climate controls and optimize crop capacity. These devices are often located in remote areas of the land , providing more accurate measurements in the growth environment and saving travel time.

Crop management: Part of the precision agriculture sector, crop management devices operate similarly to weather stations. They can be set up in the field to collect data related to crops, such as precipitation, crop health, and temperature. This allows farmers to monitor crop conditions and growth, as well as to spot any diseases or infestation that can negatively impact the crop yield.

Cattle monitoring and management: Similar to crop monitoring, IoT for cattle acts like medical wearables for livestock to monitor their performance and health. These devices can also be used for tracking to determine the location of the herd or an individual animal.

For example, sensors can alert farmers to potentially diseased animals for timely quarantine, sparing the rest of the herd. Drones can also be used for real-time livestock tracking.

Agriculture drones: Agricultural drones offer more precise data than planes or satellites. They can replace human labor in a variety of areas, including tracking livestock, combating pests, spraying, crop monitoring, and planting crops.

Predictive analytics: Predictive analytics is a component of precision agriculture. Real-time data offers many advantages for farmers, but the analytics aspect helps them use the information for better forecasting and optimization.

Overall, farming is at the whim of weather conditions, and data analytics can help farmers predict and manage the unexpected. For example, predicting upcoming precipitation can help farmers adjust irrigation to avoid over- or underwatering and optimize the use of fertilizers.

Greenhouse automation: Greenhouses are typically managed manually, but IoT sensors offer accurate, real-time information on changing greenhouse conditions, like soil quality, humidity levels, temperature, and lighting. Along with gathering data, these sensors allow controls to adjust the conditions to match the ideal parameters.

End-to-end management: This is the most complex part of agricultural IoT and includes a variety of IoT devices and sensors with analytical capabilities. Together, these devices offer remote farm monitoring and the capability to streamline operations.

The Challenges of Smart Farming

IoT for agriculture has virtually limitless applications, but it's not without its limitations.

Hardware and sensor quality for accuracy. The type of information you intend to collect should determine the hardware and sensors for your device, as well as the quality of the data you can collect.

Data analytics and predictive algorithms. Data analytics is an important component of smart agriculture, so it's vital to choose an option with robust capabilities.

Ongoing maintenance. One of the benefits of smart agriculture is that devices can be in remote or distant environments, but that also comes with challenges related to maintenance.

Mobility for field use. Similar to maintenance, smart devices should be designed for field use and enabled for remote access on a computer or smartphone.

Connectivity. Like all other smart technology, smart agriculture relies on communication. The connection must be able to travel over distances and be reliable enough for inclement weather.

Security. Smart farming involves large data sets, creating potential security vulnerabilities. Data security in IoT is an ongoing challenge, particularly in agriculture, so it's crucial to have AI-based security tools, data traffic monitoring, encryption, and remote access control.

IoT is seeing more and more adoption, but it's new in the farming industry. As farmers face greater obstacles, however, it's likely that IoT adoption will increase along with them. Young farmers are bringing about the Fourth Industrial Revolution in agriculture with future technologies that harness the power of data, automate processes, and optimize operations.