Application of generalized Hough transform for detecting sugar beet plant from weed using machine vision method

Introduction Sugar beet (Beta vulgaris L.) as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important...

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Autores principales:	A Bakhshipour Ziaratgahi, A. A Jafari, Y Emam, S. M Nassiri, S Kamgar, D Zare
Formato:	article
Lenguaje:	EN FA
Publicado:	Ferdowsi University of Mashhad 2017
Materias:	generalized hough shape processing sugarbeet visible machine vision weed Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/3ddcd6d7cf6849e38ddb6b14fc1e76d7
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id	oai:doaj.org-article:3ddcd6d7cf6849e38ddb6b14fc1e76d7
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN FA
topic	generalized hough shape processing sugarbeet visible machine vision weed Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	generalized hough shape processing sugarbeet visible machine vision weed Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040 A Bakhshipour Ziaratgahi A. A Jafari Y Emam S. M Nassiri S Kamgar D Zare Application of generalized Hough transform for detecting sugar beet plant from weed using machine vision method
description	Introduction Sugar beet (Beta vulgaris L.) as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important to efficiently eliminate weeds at early growing stages. The first step of precision weed control is accurate detection of weeds location in the field. This operation can be performed by machine vision techniques. Hough transform is one of the shape feature extraction methods for object tracking in image processing which is basically used to identify lines or other geometrical shapes in an image. Generalized Hough transform (GHT) is a modified version of the Hough transform used not only for geometrical forms, but also for detecting any arbitrary shape. This method is based on a pattern matching principle that uses a set of vectors of feature points (usually object edge points) to a reference point to construct a pattern. By comparing this pattern with a set pattern, the desired shape is detected. The aim of this study was to identify the sugar beet plant from some common weeds in a field using the GHT. Materials and Methods Images required for this study were taken at the four-leaf stage of sugar beet as the beginning of the critical period of weed control. A shelter was used to avoid direct sunlight and prevent leaf shadows on each other. The obtained images were then introduced to the Image Processing Toolbox of MATLAB programming software for further processing. Green and Red color components were extracted from primary RGB images. In the first step, binary images were obtained by applying the optimal threshold on the G-R images. A comprehensive study of several sugar beet images revealed that there is a unique feature in sugar beet leaves which makes them differentiable from the weeds. The feature observed in all sugar beet plants at the four-leaf stage was a stretched S-shaped curve at the junction of the leaf and petiole. This unique shape characteristic was used as the pattern for sugar beet detection using GHT. To implement the Hough transform in the images, a 50-member group of samples was prepared from S-shaped curve to build appropriate patterns. Desired features for the Hough transformation were extracted from the patterns. In the next step, the attempts were made to find the images for the shapes similar to each of the patterns. Results and Discussion Plants were thoroughly separated from soil and residues. The accuracy of segmentation algorithm was achieved by almost 100%. The accuracy of the generalized Hough algorithm was evaluated in two stages. In the first stage, the algorithm accuracy was assessed in detecting patterns in the images. Results showed that the accuracy of the algorithm was 96.21%. In the second stage, the algorithm was evaluated for some other test images, whereas the algorithm achieved an overall accuracy of 91.65%. In some cases, the presence of a large overlap between objects in the image reduced the detection accuracy. This was because of two main reasons; 1) high interference and ambiguity in the object edges, so that Hough transform was not able to detect the predefined patterns in the objects and, 2) weeds highly overlapped with sugar beet plants and thereby they were wrongly detected as sugar beet. However, since there is no or little interference between plants at the four-leaf stage, this interference can be eliminated by morphological operations. Due to this fact, it can be said that the results of GHT algorithm are acceptable for the detection of sugar beet in the plants close to four-leaf stage. Conclusions A special feature in the shape of sugar beet leaves was used as a criterion to distinguish between sugar beet and weeds. The results showed that by quantifying this special feature, which is an S-shaped curve near the petioles connection of beet leaves, sugar beet can be discriminated from weeds with an accuracy of 91.65 %. Recalled that this feature is a shape characteristic, therefore, the generalized Hough algorithm must be applied prior to plant canopy development, which is consistent with the critical period of weed control in sugar beet fields.
format	article
author	A Bakhshipour Ziaratgahi A. A Jafari Y Emam S. M Nassiri S Kamgar D Zare
author_facet	A Bakhshipour Ziaratgahi A. A Jafari Y Emam S. M Nassiri S Kamgar D Zare
author_sort	A Bakhshipour Ziaratgahi
title	Application of generalized Hough transform for detecting sugar beet plant from weed using machine vision method
title_short	Application of generalized Hough transform for detecting sugar beet plant from weed using machine vision method
title_full	Application of generalized Hough transform for detecting sugar beet plant from weed using machine vision method
title_fullStr	Application of generalized Hough transform for detecting sugar beet plant from weed using machine vision method
title_full_unstemmed	Application of generalized Hough transform for detecting sugar beet plant from weed using machine vision method
title_sort	application of generalized hough transform for detecting sugar beet plant from weed using machine vision method
publisher	Ferdowsi University of Mashhad
publishDate	2017
url	https://doaj.org/article/3ddcd6d7cf6849e38ddb6b14fc1e76d7
work_keys_str_mv	AT abakhshipourziaratgahi applicationofgeneralizedhoughtransformfordetectingsugarbeetplantfromweedusingmachinevisionmethod AT aajafari applicationofgeneralizedhoughtransformfordetectingsugarbeetplantfromweedusingmachinevisionmethod AT yemam applicationofgeneralizedhoughtransformfordetectingsugarbeetplantfromweedusingmachinevisionmethod AT smnassiri applicationofgeneralizedhoughtransformfordetectingsugarbeetplantfromweedusingmachinevisionmethod AT skamgar applicationofgeneralizedhoughtransformfordetectingsugarbeetplantfromweedusingmachinevisionmethod AT dzare applicationofgeneralizedhoughtransformfordetectingsugarbeetplantfromweedusingmachinevisionmethod
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spelling	oai:doaj.org-article:3ddcd6d7cf6849e38ddb6b14fc1e76d72021-11-14T06:33:54ZApplication of generalized Hough transform for detecting sugar beet plant from weed using machine vision method2228-68292423-394310.22067/jam.v7i1.49959https://doaj.org/article/3ddcd6d7cf6849e38ddb6b14fc1e76d72017-03-01T00:00:00Zhttps://jame.um.ac.ir/article_31443_9fdf7d134880be1d4105b1c4e7d08c87.pdfhttps://doaj.org/toc/2228-6829https://doaj.org/toc/2423-3943Introduction Sugar beet (Beta vulgaris L.) as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important to efficiently eliminate weeds at early growing stages. The first step of precision weed control is accurate detection of weeds location in the field. This operation can be performed by machine vision techniques. Hough transform is one of the shape feature extraction methods for object tracking in image processing which is basically used to identify lines or other geometrical shapes in an image. Generalized Hough transform (GHT) is a modified version of the Hough transform used not only for geometrical forms, but also for detecting any arbitrary shape. This method is based on a pattern matching principle that uses a set of vectors of feature points (usually object edge points) to a reference point to construct a pattern. By comparing this pattern with a set pattern, the desired shape is detected. The aim of this study was to identify the sugar beet plant from some common weeds in a field using the GHT. Materials and Methods Images required for this study were taken at the four-leaf stage of sugar beet as the beginning of the critical period of weed control. A shelter was used to avoid direct sunlight and prevent leaf shadows on each other. The obtained images were then introduced to the Image Processing Toolbox of MATLAB programming software for further processing. Green and Red color components were extracted from primary RGB images. In the first step, binary images were obtained by applying the optimal threshold on the G-R images. A comprehensive study of several sugar beet images revealed that there is a unique feature in sugar beet leaves which makes them differentiable from the weeds. The feature observed in all sugar beet plants at the four-leaf stage was a stretched S-shaped curve at the junction of the leaf and petiole. This unique shape characteristic was used as the pattern for sugar beet detection using GHT. To implement the Hough transform in the images, a 50-member group of samples was prepared from S-shaped curve to build appropriate patterns. Desired features for the Hough transformation were extracted from the patterns. In the next step, the attempts were made to find the images for the shapes similar to each of the patterns. Results and Discussion Plants were thoroughly separated from soil and residues. The accuracy of segmentation algorithm was achieved by almost 100%. The accuracy of the generalized Hough algorithm was evaluated in two stages. In the first stage, the algorithm accuracy was assessed in detecting patterns in the images. Results showed that the accuracy of the algorithm was 96.21%. In the second stage, the algorithm was evaluated for some other test images, whereas the algorithm achieved an overall accuracy of 91.65%. In some cases, the presence of a large overlap between objects in the image reduced the detection accuracy. This was because of two main reasons; 1) high interference and ambiguity in the object edges, so that Hough transform was not able to detect the predefined patterns in the objects and, 2) weeds highly overlapped with sugar beet plants and thereby they were wrongly detected as sugar beet. However, since there is no or little interference between plants at the four-leaf stage, this interference can be eliminated by morphological operations. Due to this fact, it can be said that the results of GHT algorithm are acceptable for the detection of sugar beet in the plants close to four-leaf stage. Conclusions A special feature in the shape of sugar beet leaves was used as a criterion to distinguish between sugar beet and weeds. The results showed that by quantifying this special feature, which is an S-shaped curve near the petioles connection of beet leaves, sugar beet can be discriminated from weeds with an accuracy of 91.65 %. Recalled that this feature is a shape characteristic, therefore, the generalized Hough algorithm must be applied prior to plant canopy development, which is consistent with the critical period of weed control in sugar beet fields.A Bakhshipour ZiaratgahiA. A JafariY EmamS. M NassiriS KamgarD ZareFerdowsi University of Mashhadarticlegeneralized houghshape processingsugarbeetvisible machine visionweedAgriculture (General)S1-972Engineering (General). Civil engineering (General)TA1-2040ENFAJournal of Agricultural Machinery, Vol 7, Iss 1, Pp 73-85 (2017)

Application of generalized Hough transform for detecting sugar beet plant from weed using machine vision method

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