Drones in agriculture: applications and outlook

Transcription

1 : applications and outlook Salar Bybordi PhD, DEIB - Politecnico di Milano, Luca Reggiani [email protected] PhD, Researcher, DEIB - Politecnico di Milano [email protected]

2 Outline Unmanned Aerial Vehicles Applications UAVs for agriculture Limitations Future development Conclusions

3 Unmanned Aerial Vehicles Unmanned Aerial Vehicles (UAVs), commonly known as drones, can be unpiloted aerial vehicles or remotely piloted aircrafts. Rotary wing Fixed wing [*] [o] Easier to pilot, agile maneuvering Vertical take-off and landing More efficient aerodynamics longer flights, higher speed Large space for take-off and landing [*] "Parrot AR.Drone 2.0", N. Halftermeyer Wikimedia Commons [o] "InView Unmanned Aircraft" by Fasicle, - Wikimedia Commons

4 Unmanned Aerial Vehicles UAVs are knowing a huge, increasing interest. The worldwide market of drones for civilian use: $609 million in 2014 forecast to reach $4.8 billion in 2021 Civilian sector is about 5% of the global market but the growth rate is higher. Compound annual growth rate: CAGR = 19% The defense sector has a growth rate = 5%

5 Applications Civilian applications: filmmaking, search operations, inspecting and surveying, delivering supplies, monitoring and data acquisition In the following fields: Emergency services Security Environmental protection Agriculture Engineering and architecture Media Business

6 Precision agriculture Data acquisition Remote sensing Integration with sensor networks Monitoring (fires, fields, animals ) Chemical and biological treatments

7 Precision agriculture: advantages Optimization of the treatments only where and when necessary fertilizers cut down till to % Reduction and prevention of waste water consumption in some cases till to almost 90 % Reduction of labor and material costs Reduction of pollution Small UAVs are electrical machines. Reduction of the risks Automatic and continuous analysis of the processes and field status. Prevention.

8 Remote sensing Remote sensing regards the acquisition of information about an object or surface area by means of propagated signals (e.g. electro magnetic waves as optical or microwave signals), typically emitted and/or received by aerial vehicles (e.g. satellites, aircrafts, UAVs). Main limitations of satellites and aircrafts: expensive need expertise weather dependent resolution availability of multi-temporal data

9 UAV low altitude remote sensing: main technologies Visible-band, near-infrared, multi-spectral, hyperspectral cameras Plant and soil analysis Height, growth, health, vegetation indexes. Irrigation, property, moisture, erosion Specific chemical components Thermal imaging Plant and soil analysis Irrigation, maturity, temperature Laser scanners Plant and soil analysis Height, growth, topographical maps

10 UAV low altitude remote sensing: main technologies Datacanbeacquiredin2D / 3D and as a function of time Multi-temporal analysis: a drone can repeat the survey periodically (even every few hours) in order to appreciate the variations of the field status. Opportunity of a very advanced management and organization of work, irrigation, fertilization and necessary treatments.

11 UAV low altitude remote sensing: main outputs plants soil Vegetation indices Plant growth, counting, diseases identification Impact of chemical or biological treatments Temperature and moisture Water issues and irrigation systems Ground erosion and modifications, topography Acquisition of data for insurance claims (e.g. after storms) Of course drones usage changes with the seasons

12 Remote sensing: examples Plant height and growth can be derived by Laser Scanning 3D image analysis Microwave radars. The estimated height is affected by errors around few cm. These systems are applied to corn, wheat, rice fields. ["Plodozmian" by Lesław Zimny - Wikimedia Commons]

13 Remote sensing: examples Moisture estimation Thermal cameras Visible and near-infrared reflectance Microwave sensors Multi-spectral images ["Irrigation1" by Paulkondratuk Wikimedia Commons]

14 Remote sensing: examples NDVI (normalized difference vegetation index) estimation: Spectral reflectance measurements acquired in the visible (red) and near-infrared regions Other properties can be derived from NDVI: biomass, chlorophyll concentration of leaves, plant productivity, ["SUAS_StardustII_Ndvi_sml" by Idetec uav - Wikimedia Commons]

15 Integration with sensor networks UAVs operations can be controlled by means of feedbacks (wireless signals) from a sensors network deployed on the ground. For example: the areas covered by treatments or irrigation can be controlled by ground sensors in presence of wind or in absence of precise flight plans.

16 Limitations Drone usage is weather dependant (in particular wind, rain) National regulations Italy possible till to 25 kg France possible till 150 kg US new rules in 2015 ICAO (International Civil Aviation Organization) is preparing rules for According to EASA, (European Aviation Safety Agency), «open» use could be for flights within 500 meters and maximum altitude = 150 m.

17 Future development Technological steps Increase of UAVs autonomy: automatic piloting and operations Automatic analysis for real-time decisions Increase of precision in remote sensing Advanced integration with sensor networks and robots on the ground

18 Conclusions Today UAVs are a reality in many fields of agriculture. However precision agriculture is about to know a further progress and UAVs will play a crucial role. Important savings (20% - 90%) in terms of water, chemical treatments and labor are expected. Flight regulations are an issue but UAVs, for most agriculture applications, have low weight and fly at low altitudes over uninhabited and private areas

19 Bibliography [1] C. Zhang, J. M. Kovacs, The application of small unmanned aerial systems for precision agriculture: a review, Precision Agriculture, Springer, [2] MIT Technology Review, Agricultural Drones. Relatively cheap drones with advanced sensors and imaging capabilities are giving farmers new ways to increase yields and reduce crop damage, [3] I. Colomina, P. Molina, Unmanned aerial systems for photogrammetry and remote sensing: a review, ISPRS Journal of Photogrammetry and Remote Sensing, June [4] D. Anthony, S. Elbaum, A. Lorenz, C. Detweiler, On Crop Height Estimation with UAVs, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. [5] J. Primicerio, S. F. Di Gennaro, E. Fiorillo, L. Genesio, E. Lugato, A. Matese, F. P. Vaccari, A flexible unmanned aerial vehicle for precision agriculture, Precision Agriculture, Springer, [6] L. Hassan-Esfahani, A. Torres-Rua, A. M. Ticlavilca, A. Jensen, M. McKee, Topsoil Moisture Estimation for Precision Agriculture Using Unmanned Aerial Vehicle Multispectral Imagery, 2014 IEEE International Geoscience and Remote Sensing Symposium, [7] F. G. Costa, J. Ueyama, T. Braun, G. Pessin, F. S. Osorio, P. A. Vargas, The Use of Unmanned Aerial Vehicles and Wireless Sensor Network in Agriculture Applications, 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012.