Acquisition of Agronomic Images with Sufficient Quality by Automatic Exposure Time Control and Histogram Matching

Agronomic images in Precision Agriculture are most times used for crop lines detection and weeds identification; both are a key issue because specific treatments or guidance require high accuracy. Agricultural images are captured in outdoor scenarios, always under uncontrolled illumination. CCD-based cameras, acquiring these images, need a specific control to acquire images of sufficient quality for greenness identification from which the crop lines and weeds are to be extracted. This paper proposes a procedure to achieve images with sufficient quality by controlling the exposure time based on image histogram analysis, completed with histogram matching. The performance of the proposed procedure is verified against testing images.

[1]  Francisca López-Granados,et al.  Weed detection for site-specific weed management: mapping and real-time approaches , 2011 .

[2]  Gonzalo Pajares,et al.  Support Vector Machines for crop/weeds identification in maize fields , 2012, Expert Syst. Appl..

[3]  Gonzalo Pajares,et al.  Automatic expert system based on images for accuracy crop row detection in maize fields , 2013, Expert Syst. Appl..

[4]  Albert-Jan Baerveldt,et al.  A vision based row-following system for agricultural field machinery , 2005 .

[5]  W. S. Lee,et al.  Robotic Weed Control System for Tomatoes , 2004, Precision Agriculture.

[6]  Gonzalo Pajares,et al.  Automatic detection of crop rows in maize fields with high weeds pressure , 2012, Expert Syst. Appl..

[7]  David C. Slaughter,et al.  Autonomous robotic weed control systems: A review , 2008 .

[8]  Gonzalo Pajares,et al.  A new Expert System for greenness identification in agricultural images , 2013, Expert Syst. Appl..

[9]  Suki Kim,et al.  A New Auto Exposure System to Detect High Dynamic Range Conditions Using CMOS Technology , 2008, 2008 Third International Conference on Convergence and Hybrid Information Technology.

[10]  N. D. Tillett,et al.  Automated Crop and Weed Monitoring in Widely Spaced Cereals , 2006, Precision Agriculture.

[11]  Gonzalo Pajares,et al.  Camera Sensor Arrangement for Crop/Weed Detection Accuracy in Agronomic Images , 2013, Sensors.

[12]  Navid Nourani-Vatani,et al.  Automatic camera exposure control , 2007 .

[13]  Gonzalo Pajares,et al.  Automatic expert system for weeds/crops identification in images from maize fields , 2013, Expert Syst. Appl..

[14]  V. Fontaine And T.G. Crowe,et al.  Development of line-detection algorithms for local positioning in densely seeded crops , 2006 .

[15]  Francisca López Granados.  Weed detection for site-specific weed management: Mapping and real-time approaches , 2011 .

[16]  Vincent Leemans,et al.  Application of the hough transform for seed row localisation using machine vision , 2006 .

[17]  Oliver Holub,et al.  Quantitative histogram analysis of images , 2006, Comput. Phys. Commun..

[18]  H. T. Søgaard,et al.  Determination of crop rows by image analysis without segmentation , 2003 .

[19]  Paul L. Rosin,et al.  Evaluation of global image thresholding for change detection , 2003, Pattern Recognit. Lett..

[20]  Nitin Sampat,et al.  System implications of implementing auto-exposure on consumer digital cameras , 1999, Electronic Imaging.

[21]  Tao Jiang,et al.  Multiple templates auto exposure control based on luminance histogram for onboard camera , 2011, 2011 IEEE International Conference on Computer Science and Automation Engineering.

[22]  G. Pajares,et al.  Crop Row Detection in Maize Fields Inspired on the Human Visual Perception , 2012, TheScientificWorldJournal.