Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soybean plantation

Crop growth and weed infestation in a soybean field were monitored by processing low altitude remote sensing (LARS) images taken from crane-mounted and unmanned radio controlled helicopter-mounted platforms. Images were taken for comparison between true color (R–G–B) and color-infrared (NIR) digital cameras acquired at different heights above ground. All LARS images were processed to estimate vegetation-indices for distinguishing stages of crop growth and estimating weed density. LARS images from the two platforms (low-dynamic and high-dynamic) were evaluated. It was found that crane-mounted RGBC and NIRC platforms resulted in better quality images at lower altitudes (<10 m). This makes the crane-mounted platform an attractive option in terms of specific low altitude applications at an inexpensive cost. Helicopter-mounted RGBH and NIRH images were found suitable at altitudes >10 m. Comparison of NDVIC and NDVIH images showed that NDVI values at 28 DAG (days after germination) exhibited a strong relationship with altitudes used to capture images (R2 of 0.75 for NDVIC and 0.79 for NDVIH). However, high altitudes (>10 m) decreased NDVI values for both systems. Higher R2 values (≥0.7) were also obtained between indices estimated from crane-and helicopter-mounted images with those obtained using an on-ground spectrometer, which showed an adequate suitability of the proposed LARS platform systems for crop growth and weed infestation detection. Further, chlorophyll content was well correlated with the indices from these images with high R2 values (>0.75) for 7, 14, 21 and 28 DAG.

[1]  M. D. Steven,et al.  Satellite remote sensing for agricultural management: opportunities and logistic constraints , 1993 .

[2]  N. Z. C. Chaisattapagon,et al.  Effective criteria for weed identification in wheat fields using machine vision , 1995 .

[3]  A. Buj-Bello,et al.  Characterization of a multicomponent receptor for GDNF , 1996, Nature.

[4]  John F. Reid,et al.  DEVELOPMENT OF A PRECISION SPRAYER FOR SITE-SPECIFIC WEED MANAGEMENT , 1999 .

[5]  J. V. Stafford,et al.  Implementing precision agriculture in the 21st century. , 2000 .

[6]  K. Meyer DXMRR ”-A PROGRAM TO ESTIMATE COVARIANCE FUNCTIONS FOR LONGITUDINAL DATA BY RESTRICTED MAXIMUM LIKELIHOOD , 2000 .

[7]  J. Chavas,et al.  Technological change and risk management: an application to the economics of corn production , 2003 .

[8]  Jean-Paul Chavas,et al.  Technological change and risk management: an application to the economics of corn production , 2003 .

[9]  J. Markwell,et al.  Calibration of the Minolta SPAD-502 leaf chlorophyll meter , 2004, Photosynthesis Research.

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

[11]  H. P. W. Jayasuriya,et al.  Development of a Real-time, Variable Rate Herbicide Applicator Using Machine Vision for Between-row Weeding of Sugarcane Fields , 2006 .

[12]  Céline Nauges,et al.  Technology Adoption Under Production Uncertainty: Theory and Application to Irrigation Technology , 2006 .

[13]  K. Swain,et al.  Land-use Suitability Evaluation Criteria for Precision Agriculture Adoption in a Moderately Yielding Soya bean Cropping Area in Thailand , 2007 .

[14]  H. P. W. Jayasuriya,et al.  Oil palm pest infestation monitoring and evaluation by helicopter-mounted, low altitude remote sensing platform , 2011 .