UAV imaging with low-cost multispectral imaging system for precision agriculture applications

We report on UAV(unmanned aerial vehicle)-based imaging missions over rice and corn fields in Isabela province in the Philippines. Spectral reflectance sensors were deployed for ground truthing, while aerial imagery were produced from RGB and NIR-retrofitted cameras. The ground truth sensors function as calibration points for extending the measurements over the whole imaged field. UAV-based missions deliver the high resolution and spatial scope required of imagery targeting mixed cropping regimes and multiple-farmer plots that have differing starting dates. The ledger-based monitoring systems currently employed by local government units to monitor and manage regional agriculture can be improved by such UAV imagery.

[1]  Rhia Trogo,et al.  SMS-based Smarter Agriculture decision support system for yellow corn farmers in Isabela , 2015, 2015 IEEE Canada International Humanitarian Technology Conference (IHTC2015).

[2]  Rubens Augusto Camargo Lamparelli,et al.  Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds , 2017 .

[3]  David V. Tran,et al.  The concept and implementation of precision farming and rice integrated crop management systems for sustainable production in the twenty-first century , 2007 .

[4]  Luis C. Velasquez,et al.  Implementation of a low cost aerial vehicle for crop analysis in emerging countries , 2016, 2016 IEEE Global Humanitarian Technology Conference (GHTC).

[5]  Juha Suomalainen,et al.  Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  J. Kovacs,et al.  Applications of Low Altitude Remote Sensing in Agriculture upon Farmers' Requests– A Case Study in Northeastern Ontario, Canada , 2014, PloS one.

[7]  Xiang Zhou,et al.  Evaluation of a UAV-based hyperspectral frame camera for monitoring the leaf nitrogen concentration in rice , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[8]  J. Flexas,et al.  UAVs challenge to assess water stress for sustainable agriculture , 2015 .

[9]  Austin Jensen,et al.  Development of unmanned aerial systems for use in precision agriculture: The AggieAir experience , 2015, 2015 IEEE Conference on Technologies for Sustainability (SusTech).

[10]  Mikio Umeda,et al.  Integrating remote sensing and GIS for prediction of rice protein contents , 2011, Precision Agriculture.

[11]  Nathaniel J. C. Libatique,et al.  UAV aerial imaging applications for post-disaster assessment, environmental management and infrastructure development , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[12]  Craig S. T. Daughtry,et al.  Remote Sensing With Simulated Unmanned Aircraft Imagery for Precision Agriculture Applications , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  A. Viña,et al.  Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity , 2012 .

[14]  Diego Restuccia,et al.  The Size Distribution of Farms and International Productivity Differences , 2014 .

[15]  George C. Zalidis,et al.  An autonomous multi-sensor UAV system for reduced-input precision agriculture applications , 2016, 2016 24th Mediterranean Conference on Control and Automation (MED).