Introduction
Precision agriculture workflows involve operation of aerial drones, sensors/cameras, and software applications. The goal is to gain valuable insights into your crops. In recent times, the number of growers using drones to collect crop and field data has increased significantly. Agriculture-specific applications have been developed to meet this demand. Users can plan autonomous flight missions for their drones using smartphones and tablets to survey the field and capture images. Mapping and analysis tools can then be used to create maps, analyze results, and store information. Some examples of such survey include potential environmental stressors, disease monitoring, irrigation management, forecasting the quality and yield, and comparing crop yields across different growing seasons. The information can be stored in a centralized location for easy access by users such as co-workers or consultants, from different places and times.
| Action item | Things To Be Considered |
|---|---|
| Pre-flight decision making | |
| Define your objectives for the flight | Please refer to: – Examples of Vegetation Indices – Sensor types – Drone types |
| Planning the flight | |
| To create a flight pattern on an application, plan for the actual flight. Determine the appropriate cameras/sensors for your purpose (Red Green Blue (RGB), multispectral, hyperspectral sensors, or thermal camera). Other characteristics, including, but not limited to, Field of View (FOV), camera exposure times, location light intensity, etc., may be important for your flight mission. Examine the landscape of the area to ensure the line of sight. Plan to have an observer, if necessary. | Select the area of interest in an application, the flight pattern, height, % overlap, speed, etc., which will determine how the map will be created. In general, a 75% overlap is recommended, but it may differ based on your application. RTK (Real-Time Kinematic) and/or GCP (Ground Control Points) to correct for position errors in GPS/GNSS. Be aware of the UAV’s size, configuration, and other characteristics. Depending on the type of drone, regulations and restrictions might apply. Check local flight information (no-fly zone, special flight, etc.) and communicate with the landowner, if necessary. |
| Data acquisition | |
Obtain data from the field. Make sure the weather conditions are suitable (e.g., rain, cloud cover, wind, and the sun’s angle can influence the image quality). Keep the log book to record flight conditions. | Check and update firmware for drone, calibrate drone, sensors, and GPS, etc., set maximum altitude for flight, check line of sight, weather conditions, check/set Return-To-Home button, check/set flight mode, check/set information for flight feedback on screen (in-flight and post-flight). |
| Data processing | |
| Create map(s) from collected image data. | The selection of application and service depends on: – The time you wish to spend processing data – Your budget – Service/function offered – Level and cost of technical support – Your proficiency in computer/software – Calibration methods: absolute vs relative calibration – Sensor(s) on your drone(s). Please refer to Mapping and Analysis section |
| Data analysis | |
| Derive necessary information from the map(s) Create reports from the collected/processed data to make decisions. | Type of analysis may depend on the time, resources, and cost. E.g., planning a pesticide spray most likely need a quick decision derived from a vegetative index that may require a specific sensor. Please refer to Application fees and service choice section |
| Use information | |
| Interpret the data and make decisions. | Examples: – Current plant or soil status (e.g., nitrogen or water level) to support your following actions – Comparisons of subsequent flights throughout the growing season to determine whether adjustments have been effective. – Crop yield forecast based on multiple field comparisons from different areas or years. You can share the reports with those you authorize to view the information. |
Resources
Article from Drone Deploy: Using Ground Control Points
Citation: Sergio Vélez, Mar Ariza-Sentís, João Valente, Mapping the spatial variability of Botrytis bunch rot risk in vineyards using UAV multispectral imagery, European Journal of Agronomy, Vol. 142, 2023, 126691, ISSN 1161-0301, https://doi.org/10.1016/j.eja.2022.126691. (https://www.sciencedirect.com/science/article/pii/S1161030122002398)
A Compilation of UAV Applications for Precision Agriculture
Citation: Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis, Thomas Lagkas, Ioannis Moscholios,
A compilation of UAV applications for precision agriculture,Computer Networks,Volume 172,2020,107148, ISSN 1389-1286,https://doi.org/10.1016/j.comnet.2020.107148.https://www.sciencedirect.com/science/article/pii/S138912862030116X
Citation: Delavarpour N, Koparan C, Nowatzki J, Bajwa S, Sun X. A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges. Remote Sensing. 2021; 13(6):1204. https://doi.org/10.3390/rs13061204
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