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Camera sensor arrangement for crop/weed detection accuracy in agronomic images.

Romeo J, Guerrero JM, Montalvo M, Emmi L, Guijarro M, Gonzalez-de-Santos P, Pajares G - Sensors (Basel) (2013)

Bottom Line: In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes.Accuracy of identification and detection is an important issue to be addressed in image processing.There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensor's positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others.

View Article: PubMed Central - PubMed

Affiliation: Department of Software Engineering and Artificial Intelligence, Faculty of Informatics, Complutense University, Madrid 28040, Spain. jromeo99@hotmail.com

ABSTRACT
In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes. Accuracy of identification and detection is an important issue to be addressed in image processing. There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensor's positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others. Moreover, in agricultural applications, the uncontrolled illumination, existing in outdoor environments, is also an important factor affecting the image accuracy. This paper is exclusively focused on two main issues, always with the goal to achieve the highest image accuracy in Precision Agriculture applications, making the following two main contributions: (a) camera sensor arrangement, to adjust extrinsic parameters and (b) design of strategies for controlling the adverse illumination effects.

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Image pattern used for vignetting correction.
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f5-sensors-13-04348: Image pattern used for vignetting correction.

Mentions: Figure 5 displays this pattern. Given a channel R, G and B we apply the following operation to obtain the corrected values, Rc, Gc and Bc:(2)Rc=(1+KrP)×R;Gc=(1+KgP)×G;Bc=(1+KbP)×Bwhere × denotes pixel-by-pixel multiplication instead matrix product; Kr, Kg and Kb represent the trade-off between corrections, based on the behavior of the Schneider UV/IR 486 cut-off filter [23] we have verified, with high level of satisfaction that the following values are appropriate in our experiments: Kr = 0.3; Kg = 0.0 and Kb = 0.0. This means, that only the red channel is corrected in our application where crop line and weed detection with high accuracy is the goal.


Camera sensor arrangement for crop/weed detection accuracy in agronomic images.

Romeo J, Guerrero JM, Montalvo M, Emmi L, Guijarro M, Gonzalez-de-Santos P, Pajares G - Sensors (Basel) (2013)

Image pattern used for vignetting correction.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3673087&req=5

f5-sensors-13-04348: Image pattern used for vignetting correction.
Mentions: Figure 5 displays this pattern. Given a channel R, G and B we apply the following operation to obtain the corrected values, Rc, Gc and Bc:(2)Rc=(1+KrP)×R;Gc=(1+KgP)×G;Bc=(1+KbP)×Bwhere × denotes pixel-by-pixel multiplication instead matrix product; Kr, Kg and Kb represent the trade-off between corrections, based on the behavior of the Schneider UV/IR 486 cut-off filter [23] we have verified, with high level of satisfaction that the following values are appropriate in our experiments: Kr = 0.3; Kg = 0.0 and Kb = 0.0. This means, that only the red channel is corrected in our application where crop line and weed detection with high accuracy is the goal.

Bottom Line: In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes.Accuracy of identification and detection is an important issue to be addressed in image processing.There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensor's positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others.

View Article: PubMed Central - PubMed

Affiliation: Department of Software Engineering and Artificial Intelligence, Faculty of Informatics, Complutense University, Madrid 28040, Spain. jromeo99@hotmail.com

ABSTRACT
In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes. Accuracy of identification and detection is an important issue to be addressed in image processing. There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensor's positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others. Moreover, in agricultural applications, the uncontrolled illumination, existing in outdoor environments, is also an important factor affecting the image accuracy. This paper is exclusively focused on two main issues, always with the goal to achieve the highest image accuracy in Precision Agriculture applications, making the following two main contributions: (a) camera sensor arrangement, to adjust extrinsic parameters and (b) design of strategies for controlling the adverse illumination effects.

Show MeSH
Related in: MedlinePlus