Scientific innovation in the agricultural sector typically elicits images of autonomous seeding and harvesting equipment, smart irrigation systems, or genetic engineering. However, the most impactful innovations are often those that enhance the efficiency of routine but time-consuming tasks, like crop growth monitoring, disease detection, and yield projection.
AAEON was recently presented an exciting challenge from a client seeking a drone solution capable of leveraging AI inferencing and high-resolution imaging to provide real-time data on crop health, diseases, and environmental conditions across extensive agricultural landscapes. After careful consideration, the client selected AAEON's de next-V2K8, a compact single-board computer equipped with AMD Ryzen™ Embedded V2000 Series Processors and Radeon™ Graphics.
Challenges in Getting Agricultural Drone Applications Off the Ground
The first concern the client had was the size and weight of their chosen solution, given the impact of these factors on things such as flight time, battery life, and ease of integration. That being said, the need for a system able to acquire high-resolution images, analyze vast quantities of data, and run complex AI inferencing models meant that whichever solution the client chose had to be particularly special.
Furthermore, considering the vast land area the drones had to cover, the client required a method to track and organize the data collected by area. This necessitated the integration of GPS modules to ensure precise geospatial data. Additionally, other peripheral devices like light intensity sensors were essential for monitoring environmental conditions.
Finally, the solution had to be capable of executing said tasks on the edge, while also functioning as a wireless gateway between the field and a central management hub, enabling expert interpretation of the drone's findings and facilitating actions based on any issues identified through the drone's analysis.