Role Of Big Data Analytics In Precision Farming

Big Data Analytics is digitizing the field, providing many chances to improve and streamline farming.

Fremont, CA: Farmers nowadays spend less time in the field and more time in front of their computers. More data and insights are being collected from various sensors to enable smart farming approaches as rising IoT technologies transform modern agriculture. Drones undertake real-time field analysis, while moisture levels and other soil variables are assessed. Equipment and crops are also remotely monitored. With rising demand to feed a growing population sustainably, as well as climate change and other environmental issues, agri-tech companies are turning to big data analytics to find innovative ways to grow more with less.

The big data revolution is frequently referred to as the successor to the industrial farming trend. The digitalization of farming practices, such as autonomous vehicle fleets, advanced IoT sensors, and data-driven analytics, are driving this trend. They must be lead the move from hardware to software to handle the disruptions to end-to-end farm management systems successfully.

Globally, approximately 12 million agricultural sensors are expected to be installed by 2023, according to studies. Big data is opening the door to many planting and harvesting insights, and the agriculture business is increasingly turning towards precision farming. Trends can be collected, risks can be identified, and yields can be raised with access to comprehensive agronomic data; all of this data gives OEMs and their customers unique visibility into their operations.

According to the United Nations, around a third of all food produced for human use is never consumed, and this waste is having a significant influence on climate change. It also emphasizes the enormous pressures confronting agriculture, which must produce 60 percent more food by 2050 to feed nearly 10 billion people. It is critical to developing new technologies that enable smart precision agriculture. Food can be produced intelligently using granular data on soil health, air temperature, real-time climate patterns, pesticides, and fertilizer requirements, as well as any-time intervention tools. When big data and cloud computing are combined, an ecosystem arises that provides agricultural participants with strategic access to advanced insights as well as the potential to scale quickly.