Application of Machine-Vision Technique for Identification of Weeds in Potato Fields

Afkari Sayyah, Amir Hossein; Mohammad Doust Chaman Abad, Hamid Reza; Rasekh, Mansour; Razavi, Mahsa Sadat

doi:10.22092/erams.2017.105458.1065

Document Type : Original Article

Authors

https://doi.org/10.22092/erams.2017.105458.1065

Abstract

Machine vision technology can be used to detect the location of weeds around the main crop in a field as the machine moves, in order to decrease the losses of using herbicides. The purpose of this research is to determine the accuracy of image processing method in discriminating weeds from potato crop according to color features. Therefore, by performing a research which conducted in university of Mohaghegh Ardabili research field, three factors including: environmental light condition, type of weed (Shalambig, Pichack and Wheat), and level of crop growth, were investigated on discrimination accuracy. The results of this research were showed that there are no significant differences between two types of environmental light conditions. However, the main and interactive effects of two factors of type of weeds and level of crop growth was significant on discriminating system performance. According to this study, the first stage of crop growth is the best time for the visual tests (middle of the June) and among the three types of popular weed in the region, it is possible to discriminate the wheat from potato leaves with a reasonable accuracy according to RGB color model. By this method, it is possible to determine the location of weeds around the main crop by maximum accuracy of 95% dependent to different condition of treatments.

Keywords

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Agricultural Mechanization and Systems Research

Application of Machine-Vision Technique for Identification of Weeds in Potato Fields

References

References

Volume 18, Issue 68 - Serial Number 68
September 2017
Pages 17-30

Application of Machine-Vision Technique for Identification of Weeds in Potato Fields

References

References

Volume 18, Issue 68 - Serial Number 68September 2017Pages 17-30

Volume 18, Issue 68 - Serial Number 68
September 2017
Pages 17-30