Reshaping the agricultural sector with smart agriculture
How is edge computing shaping the future of agriculture?
Edge computing in the era of digital transformation is slowly gaining momentum across many industries. It is expected to reach around 75% by 2025. Edge computing is being adopted by many industries, including the agricultural industry. This technology is helping to build the future of farming with smart farming. Although cloud infrastructure has already played an important role in the development of the agricultural sector, edge computing is winning the race in terms of speed and efficiency.
The opportunity lies in precision farming when advanced computing is applied to smart farming technologies. While using advanced computer technology, farmers depend on data to gain better control over the industry and optimize the efficiency of their operations, which translates into lower operating expenses.
Agriculture is one of the world’s critical industries and has always been slower to advance and adopt modern technologies than other industries. But now changes are taking place as digitization becomes more accessible. The agricultural sector is realizing the benefits of advanced technologies such as AI, process automation, edge computing, IoT, etc.
Edge computing is one of the evolving technologies that has the potential to transform the agricultural sector. Digitization can help overcome some of the biggest challenges in agriculture using sensors, real-time data-driven insights and actuators.
There are many examples of use cases for smart farming and farming, ranging from tracking climate change and regulating crop or livestock conditions to greenhouse automation and even farm management solutions. .
Here are some of the opportunities that edge computing can bring:
Autonomous tractors and robotic machines can operate automatically without human intervention, and this can be done using advanced computing. Tractors can communicate with nearby sensors to acquire necessary data about the surrounding environment.
Agricultural robots can assess the most efficient ways to cover the required area taking into account the type of task performed, the number of existing vehicles in the field, the size of the device, etc. Advanced computing will allow agricultural robots to use computer vision and pre-loaded terrain data and interpret information from that data. Additionally, automated tractors can automatically redirect if there is an obstacle, such as if there is an animal or a human in the way. These smart tools can perform a wide range of tasks, such as watering, weeding specific field areas when needed, or even autonomously harvesting crops.
Similar to agricultural robots, a greenhouse or even an entire farm can be put on autopilot using IoT edge computing. This indicates that the entire ecosystem can perform the tasks on its own without depending on a remote server to process the accumulated data and make decisions about daily processes like feeding livestock, watering plants, controlling temperature, humidity, light, etc. .
Edge computing will allow the farm or greenhouse to operate without depending on the connection to the main server and to make decisions based on data from local sensors. This can lead to improved process reliability and reduced waste, making farming a more sustainable process.
Agricultural IoT systems can make sophisticated decisions about possible environmental hazards or natural disasters using edge computing. Remote sensors can accumulate and examine data regarding weather or environmental changes to predict potential disasters. If there are specific indications of danger, they can immediately process the information back to the general control center. This will allow farmers to take appropriate action in real time to protect their crops.
The reliance on edge computing by company-owned IoT devices is expected to reach 6.5 billion in the coming year. Agriculture now has every opportunity to lead innovation in this area, including manufacturing, transportation, energy, retail, and healthcare. This indicates that we can predict more use cases for edge computing in agriculture soon.
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