A vision based row detection system for sugar beet

Bakker, T.; Wouters, Hendrik; Asselt, K. van; Bontsema, J.; Tang, Lie; Müller, Joachim; Straten, G. van

doi:10.1016/j.compag.2007.07.006

Cited by 139 publications

(77 citation statements)

References 12 publications

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“…Since our algorithm is based on Tillett and Hague (1999), the row recognition errors will be in the same range of 13 to 18 mm. Bakker et al (2008) reported errors of row recognition with a Hough transform algorithm between 5 and 11 mm. Both Tillett and Hague (1999) and Bakker et al (2008) do not report on the dimensions of the crop row width, although crop row width and growth stage are important parameters for weed detection and weed control systems as well.…”

Section: Crop Row Recognition and Crop Row Widthmentioning

confidence: 99%

“…Bakker et al (2008) reported errors of row recognition with a Hough transform algorithm between 5 and 11 mm. Both Tillett and Hague (1999) and Bakker et al (2008) do not report on the dimensions of the crop row width, although crop row width and growth stage are important parameters for weed detection and weed control systems as well. Tellaeche et al (2008) reported a visionbased algorithm that takes into account the crop growth stage for weed detection as well.…”

Section: Crop Row Recognition and Crop Row Widthmentioning

confidence: 99%

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Adaptive detection of volunteer potato plants in sugar beet fields

2009

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Volunteer potato is an increasing problem in crop rotations where winter temperatures are often not cold enough to kill tubers leftover from harvest. Poor control, as a result of high labor demands, causes diseases like Phytophthora infestans to spread to neighboring fields. Therefore, automatic detection and removal of volunteer plants is required. In this research, an adaptive Bayesian classification method has been developed for classification of volunteer potato plants within a sugar beet crop. With use of ground truth images, the classification accuracy of the plants was determined. In the non-adaptive scheme, the classification accuracy was 84.6 and 34.9% for the constant and changing natural light conditions, respectively. In the adaptive scheme, the classification accuracy increased to 89.8 and 67.7% for the constant and changing natural light conditions, respectively. Crop row information was successfully used to train the adaptive classifier, without having to choose training data in advance.

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Section: Crop Row Recognition and Crop Row Widthmentioning

confidence: 99%

Section: Crop Row Recognition and Crop Row Widthmentioning

confidence: 99%

Adaptive detection of volunteer potato plants in sugar beet fields

2009

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“…Accordingly, alternative or complementary techniques are required for precise navigation control. The best practice is to use an actual view of the plant lines to determine the correct direction to navigate [2,[9][10][11]. For an automobile, navigational laser scanners and camera systems are common [12]; however, laser scanners are expensive and optimized for automobile applications; thus, they have large and overlapping spots to ensure the safe detection of all potential obstacles instead of centimeter-scale resolution [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Although different formulas can be used to process NDVI signals and images, they always result in a grayscale or binary image with an adequate threshold. Both types of images can be used for high-resolution navigation control, and most high-resolution navigation applications use Hough transformations to detect plant rows [2,[9][10][11]17], the cross-correlation is a simpler approach in terms of processing power for a small embedded system. The combination of these images with a mask representing the plant line, for cross-correlation result in a precise position signal, which can be used to (lock-in) follow along the plant line.…”

Section: Introductionmentioning

confidence: 99%

Precise Navigation of Small Agricultural Robots in Sensitive Areas with a Smart Plant Camera

2015

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Most of the relevant technology related to precision agriculture is currently controlled by Global Positioning Systems (GPS) and uploaded map data; however, in sensitive areas with young or expensive plants, small robots are becoming more widely used in exclusive work. These robots must follow the plant lines with centimeter precision to protect plant growth. For cases in which GPS fails, a camera-based solution is often used for navigation because of the system cost and simplicity. The low-cost plant camera presented here generates images in which plants are contrasted against the soil, thus enabling the use of simple cross-correlation functions to establish high-resolution navigation control in the centimeter range. Based on the foresight provided by images from in front of the vehicle, robust vehicle control can be established without any dead time; as a result, off-loading the main robot control and overshooting can be avoided.

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“…Visually-Aided Localisation for Autonomous Agricultural Vehicles 27 Bossu et al 2009;Bakker et al 2008). This strategy of segmenting plants based on colour, makes use of information in multiple spectral bands and has been found to be more robust to illumination changes and shadows than using NIR cameras.…”

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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A vision based row detection system for sugar beet

Cited by 139 publications

References 12 publications

Adaptive detection of volunteer potato plants in sugar beet fields

Adaptive detection of volunteer potato plants in sugar beet fields

Precise Navigation of Small Agricultural Robots in Sensitive Areas with a Smart Plant Camera

Visually-Aided Localisation for Autonomous Agricultural Vehicles

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