Document Type : Original Article
Authors
Abstract
A large amount of herbicide is being used for controlling weeds in agricultural, lawns, sport fields on yearly basis. This causes environmental pollution and economic concerns. To reduce the use of herbicides, hand labor may be the best way of removing weeds. It is, however, costly and time consuming. In this paper, two methods of distinguishing weeds from lawns using computer vision techniques are proposed. Due to the fact that the weeds have different colors and identification of them from grass is not possible; therefore, an algorithm was developed based on the assumption that the grass area should contain more edges while the weed area is smoother than the grass area. For identification of weed/grass two methods were used, namely: Bayesian Classifier (BO) and morphology (MO. Results indicated that correct weed identification rates for MO and BO methods were 89.58% and 80.42% respectively. Furthermore, from results obtained it can be concluded that herbicide usage was reduced more than 70%, which from economical point of view as well as reduction of environmental pollution is of great importance.
Keywords
Burgos-Artizzu, X. P., Ribeiro, A., Guijarro, M. and Pajares, G. 2011. Real-time image processing for crop-weed discrimination in maize fields. Comput. Electron. Agric. 75(2): 337-346.
Burgos-Artizzu, X. P., Ribeiro, A., Tellaeche, A., Pajares, G. and Fernández-Quintanilla, C. 2009. Improving weed pressure assessment using digital images from an experience-based reasoning approach. Comput. Electron. Agric. 65, 176-185.
Burgose-Artizzu, X. P., Ribeiro, A., Pajares, G., Tellaeche, A. and Fernandez-Quintanilla, C. 2010.
Analysis of natural images processing for the extraction of agricultural elements. Image Vision Comput. 28, 138-149.
Analysis of natural images processing for the extraction of agricultural elements. Image Vision Comput. 28, 138-149.
De Rainville, F. M., Durand, A., Fortin, F. A., Tanguy, K., Maldague, X., Panneton, B. and Simard, M. J. 2014. Bayesian classification and unsupervised learning for isolating weeds in row crops. Pattern Anal. Appl. 17(2): 401-414.
Faisal, A., Hawlader, A. A. M., Bari, A. S. M., Emam, H. and Paul, K. 2012. Classification of crops and weeds from digital images: A support vector machine approach. Crop Prot. 40, 98-104.
Gan-Mor, S. and Clark, R. L. 2001. DGPS-based automatic guidance-implementation and economical analysis. Trans. ASAE. 1, 11-92.
Gonzalez, R. C.,Wood, R. E. and Eddins, S. L. 2004. Digital Image Processing using Matlab. Upper Saddle River, NJ: Pearson Prentice Hall.
Guerrero, J. M., Pajares G., Montalvo, M., Romo, J. and Guijarro, M. 2012. Support vector machines for crop/weeds identification in maize fields. Expert Syst. Appl. 39, 11149-11155.
Han, S., Zhang, Q., Ni, B. and Reid, J. F. 2004. A guidance directrix approach to visionbased vehicle guidance systems. Comput. Electron. Agric. 43(3): 179–195.
Hemming, J. and Rath, T. 2001. Computer-vision-based Weed identification under filed conditions using controlled lighting. J. Agr. Eng. Res. 78(3): 233-243.
Hernández-Hernández, J. L., García-Mateos, G., González-Esquiva, J. M. and Escarabajal-Henarejos, D. 2016. Optimal color space selection method for plant/soil segmentation in agriculture. Comput. Electron. Agric. 122, 124-132.
Ishak, A. J., Hussain, A. and Mustafa, M. M. 2009. Weed image classification using Gabor wavelet and gradient field distribution. Comput. Electron. Agric. 66(1): 53-61.
Kawamura, K., Mashita, T., Miwa, Y. and Ito, A. 1993. Developing of weeding robot (2): Development of weed detecting sensors on green area of golf course. Proceeding of JSPE. (in Japanese)
Kazmi, W., Garcia-Ruiz, F. J., Nielsen, J., Rasmussen, J. and Andersen, H. J. 2015. Detecting creeping thistle in sugar beet fields using vegetation indices. Comput. Electron. Agric. 112, 10-19.
Lee, W. S., Slaughter, D. C. and Giles, D. K. 1996. Development of a machine vision system for weed control using precision chemical application. Proceeding of ICAME-96. 3, 802-811.
Lee, W. S., Slaughter, D. C. and Giles, D. K. 1999. Robotic weed control system for tomatoes. Precis. Agric. 1, 95-113.
Li, D. M., Wang, Y. Z. and Du, B. 2009. Research on segmentation methods of weed and soil background under hsi color mode. Second International Workshop on Knowledge Discovery and Data Mining. Jan. 23-25.
Mashita, T., Miwa, Y. and Ito, A. 1992. Developing of weeding robot (1): Manufacture of weed discrimination system on golf course. Proceeding of JSPE. (in Japanese)
Montalvo, M., Guerrero, J. M., Romeo, J., Emmi, L., Guijarro, M. and Pajares, G. 2013. Automatic expert system for weeds-crops identification in images from maize fields. Expert Syst. Appl. 40(1): 75-82.
Onyango, C. M. and Marchant, J. A. 2003. Segmentation of row crop plants from weeds using colour and morphology. Comput. Electron. Agric. 39: 141-155.
Perez, A. J., Lopez, F., Benlloch, J. V. and Christensen, S. 2000. Colour and shape analysis techniques for weed detection in cereal fields. Comput. Electron. Agric. 25(3): 197-212.
Persson, M. and Astrand, B. 2008. Classification of crops and weeds extracted by active shape models. Biosyst. Eng. 100, 484-497.
Tang, J. L., Chen, X. Q., Miao, M. R. and Wang, D. 2016. Weed detection using image processing under different illumination for site-specific areas spraying. Comput. Electron. Agric. 122, 103-111.
Tellaeche, A., Pajares, G., Burgos-Artizzu, X. P. and Ribeiro, A. 2011. A computer vision approach for weeds identification through Support Vector Machines. Appl. Soft Comput. 11, 908-915.
Watchareeruetai, U., Takeuchi, Y., Matsumoto, T., Kudo, H. and Ohnishi, N. 2006a. Computer vision based methods for detecting weeds in lawns. Mach. Vision Appl. 17(5): 287-296.
Watchareeruetai, U., Takeuchi, Y., Matsumoto, T. Kudo, H. and Ohnishi, N. 2006b. Computer Vision Based Methods for Detecting Weeds in Lawns. IEEE Conference on Cybernetics and Intelligent Systems. 7-9 June. Bangkok, Thiland.
Wiles, L. J. 2011. Software to quantify and map vegetative cover in fallow fields for weed management decisions. Comput. Electron. Agric. 78(1): 106-115.
Woebbecke, D. M., Meyer, G. E., VonBargen, K. and Mortensen, D. A. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE. 38(1): 259-269.
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