Determination of row position in small-grain crops by analysis of video images

Olsen, H.J.

doi:10.1016/0168-1699(94)00044-q

Cited by 39 publications

(19 citation statements)

References 2 publications

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“…at 25 fps (frames per second), the standard video camera frame rate. Finally, some other studies deal with simpler images, taken closer to the ground and in such a way that perspective is eliminated, so that crop rows can be more easily located and the processing adapted to real-time (Olsen, 1995;Tillet and Hague, 1999;Hague and Tillet, 2001;Sogaard and Olsen, 2003;Ribeiro et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Real-time image processing for crop/weed discrimination in maize fields

Burgos-Artizzu

Ribeiro

Guijarro

et al. 2011

Computers and Electronics in Agriculture

261

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Keywords:Computer vision Precision Agriculture Weed detection Real-time image processing This paper presents a computer vision system that successfully discriminates between weed patches and crop rows under uncontrolled lighting in real-time. The system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem's mistakes. This combination produces a system that achieves very good results under a wide variety of conditions. Tested on several maize videos taken of different fields and during different years, the system successfully detects an average of 95% of weeds and 80% of crops under different illumination, soil humidity and weed/crop growth conditions. Moreover, the system has been shown to produce acceptable results even under very difficult conditions, such as in the presence of dramatic sowing errors or abrupt camera movements. The computer vision system has been developed for integration into a treatment system because the ideal setup for any weed sprayer system would include a tool that could provide information on the weeds and crops present at each point in real-time, while the tractor mounting the spraying bar is moving.

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Section: Introductionmentioning

confidence: 99%

Real-time image processing for crop/weed discrimination in maize fields

Burgos-Artizzu

Ribeiro

Guijarro

et al. 2011

Computers and Electronics in Agriculture

261

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“…Thus the algorithm would fill up to a 4-pixel square hole or an approximately circular hole of diameter 5 pixels if the corners were rounded enough. The choice of threshold (5) ensures that external boundaries of plant blobs, which are generally slightly convex over a distance of 5 pixels, are not added to. Figure 3(d) shows the final segmented plant areas.…”

Section: Image Segmentation Algorithmmentioning

confidence: 99%

Real-Time Segmentation of Plants and Weeds

1998

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“…Identification of blobs in images infested with high weeds pressure becomes unfeasible. (7) Methods based on the accumulation of green plants: Olsen (1995) proposed a method based on the consideration that along the crop row appear an important accumulation of green parts in the image. The image is grayscale transformed where green parts appear clearer that the rest.…”

Section: Revision Of Methodsmentioning

confidence: 99%

Automatic detection of crop rows in maize fields with high weeds pressure

Montalvo

Pajares

Guerrero

et al. 2012

Expert Systems with Applications

165

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This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu's method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

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Determination of row position in small-grain crops by analysis of video images

Cited by 39 publications

References 2 publications

Real-time image processing for crop/weed discrimination in maize fields

Real-time image processing for crop/weed discrimination in maize fields

Real-Time Segmentation of Plants and Weeds

Automatic detection of crop rows in maize fields with high weeds pressure

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