2023
DOI: 10.3390/su151512034
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Irrigation Scheduling for Small-Scale Crops Based on Crop Water Content Patterns Derived from UAV Multispectral Imagery

Yonela Mndela,
Naledzani Ndou,
Adolph Nyamugama

Abstract: A timely irrigation schedule for small-scale farms is imperative for ensuring optimum crop production in the wake of drought and climate change. Owing to the large number of irrigated small-scale farms that grow different crops across all seasons in the Mutale River catchment, this study sought to develop irrigation scheduling for these crops for sustainable water utilization without compromising crop yields. Unmanned aerial vehicle (UAV) images were utilized as the base from which crop water content patterns … Show more

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Cited by 4 publications
(2 citation statements)
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“…The camera calibration was performed prior to UAV take-off, using the black-white chessboard pattern MicaSense Calibrated Reflectance Panel (CRP). This process was carried out to readjust the camera with irradiance properties of the land surface during the flight period [38]. The square located at each corner of the CRP served as a positional reference point.…”
Section: Uav Sensor Calibrationmentioning
confidence: 99%
“…The camera calibration was performed prior to UAV take-off, using the black-white chessboard pattern MicaSense Calibrated Reflectance Panel (CRP). This process was carried out to readjust the camera with irradiance properties of the land surface during the flight period [38]. The square located at each corner of the CRP served as a positional reference point.…”
Section: Uav Sensor Calibrationmentioning
confidence: 99%
“…Industries are trialling this emerging technology in more and more diverse applications [8]. They can offer substantial cost saving in agriculture through crop condition monitoring [7], weed detection [9], irrigation scheduling [10] and pesticide spraying for precision agriculture [11]. UAVs can also support pasture management to optimise the rotation of grazing for cattle [12].…”
Section: Introductionmentioning
confidence: 99%