Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter

The use of unmanned aerial vehicles has been recently increasing in precision agriculture as an alternative to very costly and not readily available satellites or airborne sensors. Vegetation indices based solely on visible reflectance, which can be derived from true colour images may be a simple and cheap alternative compared to near infrared indices. A remote-controlled hexacopter with an RGB digital camera was tested for evaluating crop biomass. The hexacopter was flown over a field in which peas and oats were grown as sole crops and intercrops, fertilised with horse manure and yard–waste compost (10 t C ha −1 ). The images were taken at flowering stage. Based on the aerial photographs, the Normalised Green–Red Difference Index (NGRDI) was calculated, and related to aboveground biomass and leaf area index (LAI). The mean of NGRDI values ranged from 0.09 to 0.13 without any effect of cropping system, while the fertiliser significantly affected the yield and the corresponding NGRDI values. NGRDI values were positively and significantly correlated with the aboveground biomass ( r  = 0.58–0.78). A high autocorrelation of NGRDI, and thus biomass, was found within the treatment plots and used for block kriging to show the spatial variability in the field. No relationship was found between NGRDI and LAI in peas ( P  = 0.68) or oats ( P  = 0.15). Nevertheless, true colour images from a hexacopter and the derived NGRDI values are a cost-effective tool for biomass estimation and the establishment of yield variation maps for site-specific agricultural decision making.

[1]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[2]  E. S. Jensen Grain yield, symbiotic N2 fixation and interspecific competition for inorganic N in pea-barley intercrops , 1996, Plant and Soil.

[3]  R. Houborg,et al.  Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data , 2008 .

[4]  F. Mahler,et al.  Soil productivity management and plant growth in the Sahel: Potential of an aerial monitoring technique , 1996, Plant and Soil.

[5]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[6]  Lei Tian,et al.  Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV) , 2011 .

[7]  J. Dungan Spatial prediction of vegetation quantities using ground and image data , 1998 .

[8]  Takeshi Motohka,et al.  Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology , 2010, Remote. Sens..

[9]  C. Daughtry,et al.  Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status , 2005, Precision Agriculture.

[10]  C. Field,et al.  Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types , 1995 .

[11]  A. Viña,et al.  Remote estimation of leaf area index and green leaf biomass in maize canopies , 2003 .

[12]  张静,et al.  Banana Ovate family protein MaOFP1 and MADS-box protein MuMADS1 antagonistically regulated banana fruit ripening , 2015 .

[13]  P. C. Robert Precision agriculture: a challenge for crop nutrition management , 2002 .

[14]  N. Goel,et al.  Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation , 1994 .

[15]  Pavan Kumar,et al.  Geospatial Strategy for Tropical Forest-Wildlife Reserve Biomass Estimation , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Craig S. T. Daughtry,et al.  Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring , 2010, Remote. Sens..

[17]  Yubin Lan,et al.  Spatial Analysis of NDVI Readings with Different Sampling Densities , 2011 .

[18]  E. S. Jensen,et al.  Evaluating pea and barley cultivars for complementarity in intercropping at different levels of soil N availability , 2001 .

[19]  A. Formaggio,et al.  Influence of data acquisition geometry on soybean spectral response simulated by the prosail model , 2013 .

[20]  James W. Jones,et al.  Spatial validation of crop models for precision agriculture , 2001 .

[21]  Pierre Hiernaux,et al.  Non-destructive measurement of plant growth and nitrogen status of pearl millet with low-altitude aerial photography , 1997 .

[22]  M. Gutierrez Rodriguez,et al.  Canopy reflectance, stomatal conductance, and yield of Phaseolus vulgaris L. and Phaseolus coccineus L. under saline field conditions , 2005 .

[23]  R. Joergensen,et al.  Organic fertilizer effects on growth, crop yield, and soil microbial biomass indices in sole and intercropped peas and oats under organic farming conditions , 2014 .

[24]  Agriculture field characterization using GIS software and scanned color infrared aerial photographs , 2001 .

[25]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[26]  R. Joergensen,et al.  Permanent–soil monitoring sites for documentation of soil-fertility development after changing from conventional to organic farming , 2006 .

[27]  Jiyul Chang,et al.  Corn (Zea mays L.) Yield Prediction Using Multispectral and Multidate Reflectance , 2003 .

[28]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[29]  F. López-Granados,et al.  Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management , 2013, PloS one.

[30]  Alessandro Matese,et al.  A flexible unmanned aerial vehicle for precision agriculture , 2012, Precision Agriculture.

[31]  Tomislav Hengl,et al.  A Practical Guide to Geostatistical Mapping , 2009 .

[32]  S. Cuttle,et al.  Is the productivity of organic farms restricted by the supply of available nitrogen? , 2002 .

[33]  M. Rossi,et al.  Fine-scale spatial distribution of biomass using satellite images , 2014 .

[34]  J. E. Rasmussen,et al.  Potential uses of small unmanned aircraft systems (UAS) in weed research , 2013 .

[35]  F. Ziadat,et al.  Alternative Cropping Systems to Control Soil Erosion in the Arid to Semi-Arid Areas of Jordan , 2008 .

[36]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[37]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[38]  K. Swain,et al.  Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. , 2010 .

[39]  Edzer J. Pebesma,et al.  Multivariable geostatistics in S: the gstat package , 2004, Comput. Geosci..