Unmanned Aerial Vehicle (UAV) and Spectral Datasets in South Africa for Precision Agriculture

Cilence Munghemezulu (), Zinhle Mashaba-Munghemezulu, Phathutshedzo Eugene Ratshiedana, Eric Economon, George Chirima and Sipho Sibanda
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Cilence Munghemezulu: Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa
Zinhle Mashaba-Munghemezulu: Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa
Phathutshedzo Eugene Ratshiedana: Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa
Eric Economon: Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa
George Chirima: Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa
Sipho Sibanda: Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa

Data, 2023, vol. 8, issue 6, 1-14

Abstract: Remote sensing data play a crucial role in precision agriculture and natural resource monitoring. The use of unmanned aerial vehicles (UAVs) can provide solutions to challenges faced by farmers and natural resource managers due to its high spatial resolution and flexibility compared to satellite remote sensing. This paper presents UAV and spectral datasets collected from different provinces in South Africa, covering different crops at the farm level as well as natural resources. UAV datasets consist of five multispectral bands corrected for atmospheric effects using the PIX4D mapper software to produce surface reflectance images. The spectral datasets are filtered using a Savitzky–Golay filter, corrected for Multiplicative Scatter Correction (MSC). The first and second derivatives and the Continuous Wavelet Transform (CWT) spectra are also calculated. These datasets can provide baseline information for developing solutions for precision agriculture and natural resource challenges. For example, UAV and spectral data of different crop fields captured at spatial and temporal resolutions can contribute towards calibrating satellite images, thus improving the accuracy of the derived satellite products.

Keywords: unmanned aerial vehicles; spectral data; precision agriculture; high-resolution imagery (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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