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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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Related in: MedlinePlus

(a) Original image; (b) binary image without vignetting correction; (c) binary image with vignetting correction.
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f13-sensors-13-04348: (a) Original image; (b) binary image without vignetting correction; (c) binary image with vignetting correction.

Mentions: Regarding the process related to vignetting, Figure 13(a) displays an original image and Figure 13(b,c) the binary segmented images without and with vignetting correction. In Figure 13(b) we can easily see how an excess of white pixels appears at the four corners representing green plants, which is not present in the image of Figure 13(c) after vignetting correction. Thus, when no vignetting correction is applied, high inaccuracy results in the corners during weed and crop line detection.


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)

(a) Original image; (b) binary image without vignetting correction; (c) binary image with vignetting correction.
© Copyright Policy
Related In: Results  -  Collection

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

f13-sensors-13-04348: (a) Original image; (b) binary image without vignetting correction; (c) binary image with vignetting correction.
Mentions: Regarding the process related to vignetting, Figure 13(a) displays an original image and Figure 13(b,c) the binary segmented images without and with vignetting correction. In Figure 13(b) we can easily see how an excess of white pixels appears at the four corners representing green plants, which is not present in the image of Figure 13(c) after vignetting correction. Thus, when no vignetting correction is applied, high inaccuracy results in the corners during weed and crop line detection.

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