GIS and geospatial data, coupled with cutting-edge farm equipment, precision annotation tools, and data enrichment specialists, combine to advance farming efforts, making them more efficient and effective. The use of remote sensors has accelerated over the past few years, as even small farms have adopted more scientific methods. The geospatial data-driven focus has only intensified in recent years through increasing Research and Development expenditures and, in at least some cases, a shift away from low-cost, often crowd-sourced data annotation, in favor of teams of trained, domain-specific, data analysts who bring specific agriculture subject matter expertise to the engagement.
Download the White Paper to:
- Learn the evolution and foundational concepts of precision agriculture
- See how GIS and other geospatial data is used in precision agriculture
- Understand the mechanism behind autonomous farm equipment
- Explore IoT-based farming processes
First 300 words:
Farmers around the world have been cultivating the land for thousands of years, and throughout those millennia, there have always been the visionaries searching for new methods, ways to make the tilled earth of their fields more bountiful. From new irrigation processes to more informed crop management, farming has become a steadily more scientific enterprise, one that in the 21st Century increasingly benefits from a bird’s eye view of all those tilled acres.
Geospatial data, in fact, has become the lifeblood of advanced farming. Nowhere is that more apparent than in the evolution of so-called “precision agriculture” – the use of data and data analytics to develop more efficient farming techniques that reduce costs for everything from irrigation to the use of fertilizers, pesticides and herbicides through “as-needed” models heavily reliant on GIS and other geospatial data. The aims are the two sides of the coin for any business: increase productivity and reduce costs.
Precision agriculture’s foundational concept is that farmland is anything but a homogeneous commodity, and that critical variations in soil composition, the nutrients contained in that soil – even moisture levels and quality. All of that requires consistent measurement through all of the toolsets available to geospatial data analysts, and the consequent use of all available means of acquiring that data (remote sensors, satellite imagery, drone and other aerial imagery, GPS reckoning, and the like.
The use of remote sensors has accelerated over the past few years, as even small farms have adopted more scientific methods, including zone-based feld management that splits farm fields into different sectors based on the type(s) of crops, the type of soil composition and a multitude of other factors. Precision agriculture techniques require constant monitoring and analysis of such factors as pH rates, the presence of pest infestation, the types of…
