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Global Geology 2019, 22(2) 98-104 DOI:
10.3969/j.issn.1673-9736.2019.02.04 ISSN: 1673-9736 CN: 22-1371/P |
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Assessing applicability of Near-Infrared/Green/Blue UAV modified camera in crop monitoring: a case study of eastern Zimbabwe |
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MUREFU Mike, CHEN Shengbo |
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College of Geo-Exploration Science and Technology, Jilin University, Changchun 130061, China |
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Unmanned Aerial Vehicles (UAVs) have become popular and their use in agriculture monitoring is attracting more and more attention. There has emerged another class of agricultural UAVs whose normal consumer grade Red/Green/Blue (RBG) bands cameras have been modified to include the Near-Infrared (NIR) band by replacing one of the visible channel bands. This reduces the cost for agricultural UAVs. However, few researches have assessed the suitability of these modified UAV cameras in agricultural remote sensing. This study employed a modified UAV consumer grade camera with Blue/Green/Near Infra-red (BGNIR) bands to assess its applicability in crop remote sensing monitoring. Two experimental fields in Eastern Zimbabwe were used to assess the applicability of the modified BGNIR UAV camera in potato stress detection, maize senescence monitoring and chlorophyll concentration variations in bananas. Processed Green Normalized Vegetation Index (GNDVI) maps from the UAV imagery were compared with actual ground data of geo-tagged images taken during the UAV flights. Visual comparison between the ground and UAV imagery showed positive correlation. Highly stressed potato plants had lower GNDVI values than the healthier looking plants. Matured maize canopies also had lower GNDVI values than the late mature plants whose leaves were still green. GNDVI values in bananas from the first flight ranged from 0.094 91 to 0.334 74 and after the application of Nitrogen/Phosphorous/Potassium (NPK) fertilizer the GNDVI values ranged from 0.124 61 to 0.555 64. Increase in nitrogen also increases chlorophyll concentration in plant leaves hence the values of GNDVI increase after fertilization. We conclude that consumer grade modified UAV cameras are suitable in remote sensing of agricultural crops. Their adoption and utilization reduce the cost burden on farmers in developing countries especially in Africa, and help them to monitor their crops more efficiently. |
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chlorophyll concentration
crop stress
GNDVI
senescence
UAV
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Assessing applicability of Near-Infrared/Green/Blue UAV modified camera in crop monitoring: a case study of eastern Zimbabwe |
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MUREFU Mike, CHEN Shengbo |
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College of Geo-Exploration Science and Technology, Jilin University, Changchun 130061, China |
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Abstract:
Unmanned Aerial Vehicles (UAVs) have become popular and their use in agriculture monitoring is attracting more and more attention. There has emerged another class of agricultural UAVs whose normal consumer grade Red/Green/Blue (RBG) bands cameras have been modified to include the Near-Infrared (NIR) band by replacing one of the visible channel bands. This reduces the cost for agricultural UAVs. However, few researches have assessed the suitability of these modified UAV cameras in agricultural remote sensing. This study employed a modified UAV consumer grade camera with Blue/Green/Near Infra-red (BGNIR) bands to assess its applicability in crop remote sensing monitoring. Two experimental fields in Eastern Zimbabwe were used to assess the applicability of the modified BGNIR UAV camera in potato stress detection, maize senescence monitoring and chlorophyll concentration variations in bananas. Processed Green Normalized Vegetation Index (GNDVI) maps from the UAV imagery were compared with actual ground data of geo-tagged images taken during the UAV flights. Visual comparison between the ground and UAV imagery showed positive correlation. Highly stressed potato plants had lower GNDVI values than the healthier looking plants. Matured maize canopies also had lower GNDVI values than the late mature plants whose leaves were still green. GNDVI values in bananas from the first flight ranged from 0.094 91 to 0.334 74 and after the application of Nitrogen/Phosphorous/Potassium (NPK) fertilizer the GNDVI values ranged from 0.124 61 to 0.555 64. Increase in nitrogen also increases chlorophyll concentration in plant leaves hence the values of GNDVI increase after fertilization. We conclude that consumer grade modified UAV cameras are suitable in remote sensing of agricultural crops. Their adoption and utilization reduce the cost burden on farmers in developing countries especially in Africa, and help them to monitor their crops more efficiently. |
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Keywords:
chlorophyll concentration
crop stress
GNDVI
senescence
UAV
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DOI: 10.3969/j.issn.1673-9736.2019.02.04 |
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Supported by Co-building Project of Jilin Province and Jilin University (No. SXGJXX2017-2). |
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Bojovic B, Markovic A. 2009. Correlation between nitrogen and chlorophyll content in wheat.Kragujevac Science Journal,31:69-74.
Bulanon D M, Lonai J, skovgard H,et al. 2016. Evaluation of different irrigation methods for an apple orchard using an aerial imaging system.ISPRS International Journal of Geo-Information,5(6):79-90. doi.org/410.3390/ijgi5060079
de Melo A S, Silva C D, Fernandes P D,et al. 2014. Chlorophyl and macronutrients content in leaf tissue of Musa sp "Prata-Ana" under fertigation.African Journal of Agricultural Research,9:1714-1720.
Gibson-Poole S, Humphris S, Tooth I,et al. 2017. Identification of the onset of diseases within a potato crop using UAV equipped with unmodified and modified commercial off-the-shelf digital cameras.Advances in Animal Biosciences:Precision Agriculture,8:2812-2816.
Gitelson A A, Merzlyak M N. 1998. Remote sensing of chlorophyll concentration in higher plant leaves.Advances in Space Research Elsevier Science,22:689-682.
Huang Y, Sui R, Thomson S J,et al. 2013. Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multispectral imagery.International Journal of Agriculture & Biological Engineering,6:37-41.
Hunt R E, Doraiswamy P C, Murtrey J E,et al. 2013. A visible band index for remote sensing leaf chlorophyll content atcanopy scale.International Journal of Applied Earth Observation and Geoinformation,21:103-112.
Hunt R E, Hively D W, Fujikawa S J,et al. 2010. Acquisition of NIR-Green-Blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing,2(1):290-305. https://doi.org/10.3390/rs2010290
Machovina, B L, Feeley K J, Machovina B J. 2016. UAV remote sensing of spatial variation in banana production. Crop and Pasture Science,67:1281-1287. https://doi.org/10.1071/CP16135
Makanza R, Zaman-Allah M, Cairns J E,et al. 2018. High-throughput phenotyping of canopy cover and senescence in maize field trials using aerial digital canopy imaging. Remote Sensing,10:330-342. https://doi.org/10.33390/rs10020330
Maresma A, Ariza M, Martinez E,et al. 2016. Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays L.) from a standard UAV service. Remote Sensing,8:973-987. https://doi.org/10.3390/rs8120973
Rasmussen J, Ntakos G, Nielsen J,et al. 2016. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? European Journal of Agronomy,74:75-92.
Yang G, Liu J, Zhao C,et al. 2017. Unmanned aerial vehicle remote sensing for field-based crop phenotyping:current status and perspectives.Frontiers in Plant Science,8:1-26.
Zhang C, Kovacs J M. 2012. The application of small unmanned aerial systems for precision agriculture:a review.Precision Agriculture,13:693-712. https://doi.org/10.1007/s11119-012-9274-5 |
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