Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption

A low-altitude remote sensing (LARS) system with an unmanned radio-controlled helicopter platform was used to acquire high-quality images of land and crop properties with higher spatial and temporal resolution. It is vital to visualize the relationship of LARS-based images with crop parameters, such as crop nutrient levels, etc. Five N-treatment (0, 33, 66, 99 and 132 kg ha -1) rates with three replications each were arranged in a randomized manner for testing the LARS image acquisition system. Images were taken by the image acquisition unit of the system operated at a height of 20 m over the experimental plots. The coefficient of determination (r 2) between N-treatments against NDVI lars, NDVI spectro, GNDVI lars, and chlorophyll content estimated from leaf radiance values were in the range from 0.70 to 0.90, showing a high level of correlation between them. The test to verify the suitability of LARS-based images against spectrophotometer readings showed linear variation for the NDVI index with r 2 of 0.70 and 0.80 for 45-day-old and 65-day-old crops, respectively, Linear models were also developed to estimate chlorophyll content from NDVI lars and GNDVI lars index values, from the images, with better correlation for the latter (r 2 ≈ 0.82) and subsequently could determine the nitrogen deficiency level. The yield estimation model, with higher r 2 values of 0.95 and 0.98 for NDVI lars and GNDVI lars, respectively, further justified the suitability of the LARS system.

[1]  Shufeng Han,et al.  Sensor Based Variable Rate Application of Nitrogen by Using a Multi-Spectral Image Sensor , 2004 .

[2]  C. W. Wood,et al.  Relationships between chlorophyll meter readings and leaf chlorophyll concentration, N status, and crop yield: A review 1 , 1993 .

[3]  Paul Fouché,et al.  Assessment of crop stress conditions using low altitude aerial color‐infrared photography and computer image processing , 1994 .

[4]  Niels O. Maness,et al.  Chlorophyll Estimation Using Multi-spectral Reflectance and Height Sensing , 2004 .

[5]  H. Eisenbeiss.  A MINI UNMANNED AERIAL VEHICLE (UAV): SYSTEM OVERVIEW AND IMAGE ACQUISITION , 2004 .

[6]  Kazunobu Ishii,et al.  The Development of Remote Sensing System Using Unmanned Helicopter , 2002 .

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

[8]  J. S. Aber,et al.  Patterns in Estonian bogs as depicted in color kite aerial photographs , 2002 .

[9]  Mikio Umeda,et al.  Estimation of nitrogen content using machine vision in a paddy field. , 2000 .

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

[11]  John A. Gamon,et al.  Assessing leaf pigment content and activity with a reflectometer , 1999 .

[12]  Lei Tian,et al.  Development of autonomous unmanned helicopter based agricultural remote sensing system , 2006 .

[13]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[14]  J. F. Reid,et al.  MODELING AND CALIBRATION OF A MULTI-SPECTRAL IMAGING SENSOR FOR IN-FIELD CROP NITROGEN ASSESSMENT , 2006 .

[15]  Cheng‐Wei Huang,et al.  Determination of Nitrogen Content in Rice Crop Using Multi-Spectral Imaging , 2003 .

[16]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

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

[18]  U. Schmidhalter,et al.  An on-farm communication system for precision farming with nitrogen real-time application , 1999 .

[19]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .