Determination of crop rows by image analysis without segmentation

Søgaard, H. T.; Olsen, H.J.

doi:10.1016/s0168-1699(02)00140-0

Cited by 215 publications

(110 citation statements)

References 9 publications

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“…The following is a list of crop row detection methods grouped into different categories including the above. Sogaard and Olsen (2003) apply RGB color image transformation to gray scale. This is done by first dividing the color image into its red, green and blue channels and then by applying the well-tested methods to extract living plant tissue described in Woebbecke, Meyer, von Bargen, and Mortensen (1995).…”

Section: Revision Of Methodsmentioning

confidence: 99%

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Automatic expert system based on images for accuracy crop row detection in maize fields

Guerrero

Guijarro

Montalvo

et al. 2013

Expert Systems with Applications

108

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This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil-Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product-moment correlation coefficient.

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Section: Revision Of Methodsmentioning

confidence: 99%

“…Some templates are used to guide the detection. Linear regression is also applied in Sogaard and Olsen (2003). Linear regression is highly sensitive to isolated weeds patches placed on the inter crop rows and also for weeds patches overlapped with crops.…”

Section: Methods Based On Linear Regressionmentioning

confidence: 99%

Automatic expert system based on images for accuracy crop row detection in maize fields

Guerrero

Guijarro

Montalvo

et al. 2013

Expert Systems with Applications

108

View full text Add to dashboard Cite

show abstract

“…The vision-based error was in range of 50-300 mm, while the error of the DGPS was in a range from 40 to 60 mm. Søgaard and Olsen (2003) mounted a camera on a hand-operated vehicle and later on a weeder to evaluate the precision of an algorithm based on image analysis. The camera height was of 1.15 m and the inclination of the optical axis on the vertical of 56°.…”

Section: Introductionmentioning

confidence: 99%

A computer-vision based precision seed drill guidance assistance

Leemans

Destain

2007

Computers and Electronics in Agriculture

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This paper presents a control mechanism aiming to position seed drills relative to the previous lines, while sowing. The position was measured by a machine vision system and used in a feedback control loop. An articulated mechanism was used to ensure the lateral displacement of the drill relative to the tractor. The behaviour of the whole outfit was studied during several field tests. The standard deviation of the error, measured as the difference between the observed inter-row distance and its set value, was 23 mm and its range was less than 100 mm, which was sufficient to fulfil the requirements of the application. Sources of systematic errors were also identified as linked to the geometric considerations. Their correction requires an accurate mounting of the camera, which may be possible for a serial montage.

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“…They summed the pixel intensity values in the direction of the columns to create a "sum-curve", then low-pass filtered this signal before locating the crop rows at the local maxima of this curve. Søgaard & Olsen (2003) divided a green-enhanced image into a number of horizontal strips, and calculated the centre of each crop row within each strip with a "centre of gravity" procedure, before using linear regression to fit lines to these points. Hague & Tillett (2001) also used a series of horizontal strips and linear regression to fit crop rows, but instead found peaks representing the centre of the crop rows by applying a spatial bandpass filter to each horizontal strip.…”

Section: Monocular Vision Crop Row Trackingmentioning

confidence: 99%

Visually-Aided Localisation for Autonomous Agricultural Vehicles

English¹

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This thesis presents an approach to visually-aided navigation of agricultural robots in cropping fields.Many agricultural robots and vehicles navigate solely using sophisticated RTK-GPS/INS systems, however these systems remain expensive or are prone to signal occlusion. Alternatively, navigation in agricultural fields can be performed relative to crop rows using local sensors such as cameras. However, current visual crop row tracking algorithms are applicable to only a subset of fields since they make strong and restrictive assumptions about the appearance of the crop. Additionally, current navigation filters for agricultural robots do not fuse crop row tracking together with global navigation sensors to improve their position estimate.This thesis proposes a localisation system suitable for small agricultural robots that augments low-cost GPS and odometry sensors with novel visual crop row tracking methods. In working towards this goal this thesis provides two main contributions.

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Determination of crop rows by image analysis without segmentation

Cited by 215 publications

References 9 publications

Automatic expert system based on images for accuracy crop row detection in maize fields

Automatic expert system based on images for accuracy crop row detection in maize fields

A computer-vision based precision seed drill guidance assistance

Visually-Aided Localisation for Autonomous Agricultural Vehicles

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