Almost every aspect of our lives has the potential to be transformed by technology. In various industries, it improves efficiency by reducing workload and required time. There is no exception in the agricultural industry. Agriculture labor productivity has increased significantly due to the progressive automation of agricultural processes, which has shifted large numbers of workers into other productive industrial areas. Scientific advances in chemistry, genetics, robotics, and various other applied sciences have fueled the rapid advancement of agricultural technology since then.
However, demand for agricultural products is expected to rise even more, with estimates predicting a 69 percent increase in aggregate agricultural consumption from 2010 to 2050, fueled primarily by a rise in the global population from 7 to 9 billion people during the same period.
The technology sector must be the only viable response to this urgent call for increased agricultural production. Drone technology and advanced image data analytics, along with the capabilities it provides, have the potential to become important components of the technology mix that can bridge the gap between current agricultural production and future needs.
Potential of drones in agriculture
In the agriculture industry, drone technology combined with advanced image data analytics can be used for various applications. According to our most recent estimates, the addressable market for agricultural drone applications is worth USD 32.4 billion. Drones are used in most applications as a mobile, aerial platform for advanced image data acquisition. Drones can be outfitted with various image data sensors depending on the project’s needs. Assessing the health of crop vegetation is the most well-known application based on drone-acquired image data. The Normalized Difference Vegetation Index development can be aided by an unmanned airborne platform equipped with infrared cameras (NDVI).
Using an NDVI-view of a specific area makes it possible to analyze the intensity of solar radiation absorption and the condition of the monitored plants. This method relies on satellite or plane-borne cameras and has been widely used for decades. Nonetheless, the resolution of the resulting products was insufficient to precisely map fields, let alone specific plants.
As a platform for image data acquisition, drone technology has taken NDVI mapping to a whole new level of precision, allowing for monitoring plant health and specific parts of plants. Early detection of pests, diseases, and pests is possible with this level of information. Precision fertilizer, pesticide, or herbicide applications can address the precisely mapped and identified issues within a given area. In natural disasters or crop destruction, advanced geospatial NDVI products can precisely estimate the level of losses by comparing the pre-disaster state of vegetation with the damages that occurred. In insurance procedures, precise documentation of damages followed by precise estimation of the reduction in estimated yields can be used.
Drone technology is increasingly being used in the insurance industry, with agriculture claims management being one of the most common uses. Counting and inventorying herds of animals is another less obvious application of drone imaging and mapping capabilities. Every animal is a separate heat mark with high-resolution infrared cameras, allowing for more accurate counting than traditional methods. More sophisticated tasks are now possible thanks to the advancement of infrared camera applications in herd monitoring. Focusing a high-resolution infrared camera on a single animal makes it possible to assess its health based on a temperature comparison, allowing for quick identification and treatment of sick animals.
Crop spraying is another application of drone technology in agriculture. In the 1980s, unmanned helicopters equipped with spraying equipment and pesticide tanks were used to spray crop fields in Japan for the first time. Modern spraying drones typically have a tank capacity of over ten liters of liquid pesticide and a discharge rate of over one liter per minute, allowing them to cover a hectare in less than ten minutes. However, to fully utilize drone technology as a spraying platform, the spraying must be paired and synchronized with imaging, processing, and automated analytics capabilities to address the affected areas or plants precisely. This approach would improve dosage in the affected areas, but it would also reduce the overall use of chemicals.
Drone platforms with mapping and imaging capabilities and various sensors can be used throughout the manufacturing process to better plan production and increase productivity. Drone technology can assess soil conditions and, thus, potential yields before the vegetation cycle begins. Actual 3D terrain mapping with precise soil color coverage is the most important application in assessing soil conditions—this aids in the precise assessment of soil quality, moisture, and water flow. During the vegetative period, cyclical flights can monitor crops and the agricultural process to plan operations and respond quickly to any issues. Automated drones with spraying capabilities can accomplish this in an instant.
Drone-enabled NDVI index values aid in determining the precise harvesting timing. The fusion of advanced aerial information obtained with the help of drones with data from sources like weather forecasts and soil maps can help refine the final information, allowing the farmer to maximize yields to their natural limits. Drones can reach difficult-to-access and remote areas such as terrace rice fields or fruit plantations in mountainous regions, especially for specific farms in the Asia-Pacific region.
The drone technology sector and image data processing and analytics are all in constant change and development. In the coming years, several technologies in the development pipeline have the potential to transform the industry. This would almost certainly result in the rapid development of new agricultural applications or a greater impact of UAV technology in existing ones.
The combination of a drone as a platform for various sensors and machine learning-based intelligent processing and analysis software would open up a virtually limitless range of possibilities, maximizing production while reducing manned workload even more. This would result in increased productivity and lower agricultural product prices, allowing the gap between current production and the needs of the world’s growing population to be bridged.
Challenges
Advanced image data analytics and processing, on the other hand, present a challenge in terms of data strategy and management. The most difficult aspect of data management is that as information becomes more accurate and precise, the size of datasets grows in lockstep, resulting in up to 140 GB of data for a single square kilometer with a ground sampling distance (GSD) of 1 centimeter. You’ll need a data strategy tailored to your specific needs to address this problem.
Another major challenge is integrating drone-borne imaging, advanced image data processing, and analytics into existing agricultural processes so that the sector can fully benefit from new data. Having more knowledge and analytics tools will not improve your situation. New information must be implemented and integrated into agricultural business processes to fully realize the potential of drones and advanced image data analytics in the agricultural industry.
