Use of Drones in Digital Agriculture

In the digitalizing agricultural sector, unmanned aerial vehicles (UAVs), commonly known as drones, are used to increase efficiency in agricultural production, accelerate decision-support processes, and optimize resource utilization. Drones provide high-resolution data in numerous applications such as mapping agricultural land, detecting diseases and pests, monitoring plant growth, analyzing water stress, and creating three-dimensional models.

A drone consists of mechanical components such as the main body, motors, propellers, battery, and landing gear, along with electronic components including flight controllers, GPS systems, sensors, and cameras. It operates based on four fundamental movements: vertical motion, roll, pitch, and yaw. This structure enables precise measurement and data collection in agricultural activities.

Drones used for agricultural purposes typically carry the following types of sensors and cameras:

• RGB Cameras: Capture visible light to generate orthomosaic maps.
• Multispectral Cameras: Used to analyze plant health and development.
• Hyperspectral Cameras: Enable lesion detection, soil structure analysis, and quality assessment.
• Thermal Cameras: Identify plant water stress and diseases.
• LIDAR and SAR Systems: Used for 3D modeling, land classification, and positioning.

Drones are effectively utilized in the following agricultural applications:

• Disease and pest monitoring: Enables early detection through spectral sensors.
• Plant growth monitoring: Tracks crop health using indices such as NDVI and similar metrics.
• Three-dimensional modeling: Analyzes plant volume and dimensions using photogrammetric software.
• Spraying and fertilization: Achieves input savings through variable-rate application.

Through drone usage in digital agriculture:

• Real-time and detailed data can be collected across the entire production area,
• Human-related errors are minimized,
• Resource consumption (water, fertilizer, pesticides) is reduced to a minimum,
• Production decisions are optimized based on data.

The future of drone usage is expected to be shaped by the integration of artificial intelligence, machine learning, and autonomous decision-making systems. In this context, fully autonomous drone systems that operate in conjunction with decision-support systems and are guided by real-time data are anticipated to become widespread. Additionally, wireless energy transfer, enhanced sensor sensitivity, and designs resilient to adverse weather conditions are expected to further support the more effective use of drones in agriculture in the future.