نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه کشاورزی و منابع طبیعی رامین خوزستان

2 استادیار دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه کشاورزی و منابع طبیعی رامین خوزستان

چکیده

یکی از هدف­های کشاورزی دقیق به حداقل رساندن حجم علف­کش با استفاده از سیستم­های مدیریتی علف­های هرز است. برای رسیدن به این هدف، سیستمی مبتنی بر پردازش تصویر به­منظور تشخیص علف­های هرز در شرایط کنترل شده توسعه یافت. در این روش پیشنهادی از فضای رنگی HSV برای ایجاد تمایز بین پوشش گیاهیو پس­زمینه و بین محصول مورد نظر و علف­های هرز استفاده گردید. در این پژوهش از کانال رنگی H از فضای رنگی HSV برای آستانه­بندی خودکار و برخط پس­زمینه (خاک) و پیش­زمینه (علف­های هرز) استفاده شد که با اعمال فرسایش و اتساع مناسب، ناحیۀ مربوط به علف­های هرز و خاک شناسایی و آستانه­بندی گردید. بر اساس نتایج به­دست ­آمده از آزمایش تشخیص علف­های هرز/چغندرقند، عملکرد (میزان تشخیص صحیح علف­های هرز) 94 درصد مشاهده شد. این سامانۀ هوشمند، در مقایسه با سمپاش­های معمولی (سمپاش Buferagri)، در آزمایش مزرعه­ای به­دلیل استفاده از فناوری بینایی کامپیوتر، مصرف محلول سم را 86/67 درصد کاهش داد. بنابراین استفاده از این روش به عنوان یک سامانه سمپاش هوشمند در مزارع چغندرقند پیشنهاد می­شود.

کلیدواژه‌ها

Afkari-Sayyah, A. H., Mohammaddost-Chamanabad, H. R., Rasekh, M. and Sadat-Razavi, M. 2017. Application of machine-vision technique for identification of weeds in potato fields. Syst. Res. Agric. Mech. 18 (68): 17-30. (in Persian)
 
Akhil, A., Ankit, G., Nitesh, B., Shashwat, M. and Arnab, B. 2012. A plant identification system using shape and morphological features on segmented leaflets: Team IITK, CLEF 2012. CLEF 2012 Eval. Labs Work. Online Work. Notes 1-14.
 
Anon. 2014. Research institute of plant improvement beet seed performance report prepared in 2013. Sugar Beet Seed Plant Improvement. Perdue University Press, India.
 
Astrand, B. and Baerveldt, A. J. 2002. An agricultural mobile robot with vision-based perception for mechanical weed control. Auton. Robots. 13 (1): 21-35.
 
Bai, X., Zhiguo, C., Wang, Y., Yu, Z., Hu, Z., Zhang, X. and Li, C. 2014. Vegetation segmentation robust to illumination variations based on clustering and morphology modelling. Biosyst. Eng. 125, 80-97.
 
Bakhshipour-Ziyaratgahi, A., Jafari, A., Imam, Y., Nasiri, S. M., Kamgar, S. and Zare, D. 2017. Application of generalized hough transformation in diagnosis of sugar beet from herb weeds using visual machine. Agric. Mach. J. 7(1): 73-85.
 
Bradski, G. R. 1998. Computer vision face tracking for use in a perceptual user interface. Intel. Technol. J. 2, 12-21.
 
Camargo, A. and Smith, J. S. 2009. An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst. Eng. 10, 29-21.
 
Chaves-González, J. M., Vega-Rodríguez, M. A., Gómez-Pulido, J. A. and Sánchez-Pérez, J. M. 2010. Detecting skin in face recognition systems: a colour spaces study. Digit. Signal Process. A Rev. J.
20, 806-823.
 
Cho, S. I., Lee, D. S. and Jeong, J. Y., 2002. Weed-plant discrimination by machine vision and artificial neural network. Biosyst. Eng. 83, 275-280.
 
Ding, L. and Goshtasby, A. 2001. On the canny edge detector. Pattern Recogn. 34(3): 721-725.
 
Everingham, M., Gool, L. V., KI-Williams, C., Winn, J. and Zisserman, A. 2010. The pascal visual object classes (voc) challenge. Int. J. Comput. Vision. 88(2): 303-338.
 
Garcia, C. and Tziritas, G. 1999. Face detection using quantized skin color regions merging and wavelet packet analysis. IEEE Trans. Multimed. 1(3): 264-277.
 
Geiger, A., Lenz, P. and Urtasun, R. 2012. Are we ready for autonomous driving? the KITTI vision benchmark suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 16-21. Washington, DC, USA.
 
Gianessi, L. P. 2014. Importance of pesticides for growing maize in Latin America. International Pesticide Benefits Case Study No. 109. Crop Protection Research Institute, Crop Life Foundation. Available at: http//:croplife.org.
 
Grichar, W. J. and Colburn, A. E. 1993. Effect of dinitroaniline herbicides upon yield and grade of five runner cultivars. Peanut. Sci. 20, 126-128.
 
Guerrero, J. M., Pajares, G., Montalvo, M., Romeo, J. and Guijarro, M. 2012. Support vector machines for crop/weeds identification in maize fields. Expert Syst. Appl. 39(12): 11149-11155.
 
Guo, W., Rage, U. K. and Ninomiya, S. 2013. Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model. Comput. Electron. Agric. 96, 58-66.
 
Hague, T., Tillet, N. and Wheeler, H. 2006. Automated crop and weed monitoring in widely spaced cereals. Precis. Agric. 1(1): 95-113.
 
Hamuda, E., Glavin, M. and Jones, E. 2016. A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 125, 184-199.
 
Haug, S., Michaels, A., Biber, P. and Ostermann, J. 2014. Plant classification system for crop/weed discrimination without segmentation. Proceedings of the IEEE Winter Application and Computer Vision Conference. March 24-26. Steamboat Springs, CO, USA
 
Huang, M. and He, Y. 2007. Crop and Weed Image Recognition by Morphological Operations and ANN Model. Instrumentation and Measurement Technology Conference – IMTC. May 1-3. Warsaw, Poland.
 
Huang, W., Kim, K., Yang, Y. and Kim, Y. S. 2015. Automatic shadow removal by illuminance in HSV color space. Comput. Sci. Inf. Technol. 3, 70-75.
 
Jafari, A., Mohtasabi, S. S., Eqbali-Jahromi, H. and Omid, M. 2006. An appropriate algorithm for isolation of weeds from sugar beet in field real estate by using image processing. J. Agric. Sci. Iran.
37(4): 56-572. (in Persian)
 
Jihada-Akbar, M. R., Tabatabaei-Nimavaed, R. and Ebrahamian, H. D. 2004. Critical period of weed competition with sugar beet in Kabotarabad-Esfahan. Sugarbeet. 20(1): 73-92. (in Persian)
 
Kadir, A. 2014. A model of plant identification system using GLCM, lacunarity and shen features. Res. J. Pharm. Biol. Chem. Sci. 5, 1-10.
 
Kataoka, T., Kaneko, T., Okamoto, H. and Hata, S. 2003. Crop growth estimation system using machine vision. Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics. July 20-24. Kobe, Japan.
 
Kiani, S. and Jafari, A. 2012. Crop detection and positioning in the field using discriminant analysis and neural networks based on shape features. J. Agric. Sci. Technol. 14, 755-765. (in Persian)
 
Lamm, R. D. 2000. Robotic weed control for cotton. Ph. D. Thesis. Department of Biological and Agricultural Engineering, University of California, USA.
 
Lee, W. S. 1998. Robotic weed control system for tomatoes. Ph. D. Thesis. University of California, USA.
 
Lu, H., Cao, Z., Xiao, Y., Fang, Z., Zhu, Y. and Xian, K. 2015. Fine-grained maize tassel trait characterization with multi-view representations. Comput. Electron. Agric. 118, 143-158.
 
Meyer, G. E., Mehta, T., Kocher, M. F., Mortensen, D. A. and Samal, A. 1998. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Trans. ASAE. 41(4): 1189-1197.
 
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, 75-82.
 
Neto, J. C. 2004. A combined statistical-soft computing approach for classification and mapping weed species in minimum-tillage systems unpublished. Ph. D. Thesis. University of Nebraska, Lincoln, NE.
 
Otsu, N. 1979. A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9, 62–66.
 
Palaniappan, R. 1999. Regular moment analysis for pattern recognition. M. Sc. Thesis. University of Malaya, Malaysia.
 
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.
 
Phung, S. L., Bouzerdoum, A. and Chai, D. 2005. Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27,148-154.
 
Roy, J. C., Boulard, T., Kittas, C. and Wang, S. 2002. PA-precision agriculture. Biosyst. Eng. 83, 1-20.
 
Ruiz-Ruiz, G., Gómez-Gil, J. and Navas-Gracia, L. M. 2009. Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Comput. Electron. Agric. 68,
88-96.
 
Saravanakumar, S. and Ahmed, C. G. S. 2011. Multiple Object Tracking using HSV Color Space 247–252.
 
Schuster, I., Nordmeyer, H. and Rath, T. 2007. Comparison of vision-based and manual weed mapping in sugar beet. Biosyst. Eng. 98, 17-25.
 
Seddik, H. 2014. A new family of Gaussian filters with adaptive lobe location and smoothing strength for efficient image restoration. EURASIP J. Adv. Sig. Process. doi: 10.1186/1687-6180-2014-25.
 
Slaughter, D. C., Giles, D. K. and Downey, D. 2008. Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61(1): 63-78.
 
Sobottka, K. and Pitas, I. 1996. Face localization and facial feature extraction based on shape and color information. Proceedings of the International Conference on Image Processing. Sep. 16-19. Lausanne, Switzerland.
 
Sogaard, H. T. 2005. Weed classification by active shape models. Biosyst. Eng. 91 (3): 271-281.
 
Ul-Haq, M. I., Naeem, A. M., Ahmad, I. and and Islam, A. 2007. Radon Transform Based Real-Time Weed Classifier. Proceedings of the Computer Graphics, Imaging and Visualisation-IEEE Xplore. Aug.
14-17. Bangkok, Thailand.
 
Vesali, S. and Kamarizadeh, M. E. 2010. Designing a visuity algorithm for potato sprayer robot spraying. 6th National Congress on Agricultural Machinery and Mechanization. Sep. 15-16. Tehran, Iran.
(in Persian)
 
Woebbecke, D., Meyer, G., Von-Bargen, K. and Mortensen, D. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE. 38(1): 271-281.
 
Yang, W., Wang, S., Zhao, X., Zhang, J. and Feng, J. 2015. Greenness identification based on HSV decision tree. Inf. Process. Agric. 2, 149-160. Doi.10.1016/j.inpa.2015.07.003.
 
Yu, Z., Cao, Z., Wu, X., Bai, X., Qin, Y., Zhuo, W., Xiao, Y., Zhang, X. and Xue, H. 2013. Automatic image-based detection technology for two critical growth stages of maize. Emerg. Three-leaf Stage Agric. Forest Meteorol. 174-175, 65–84.
 
Zhang, L., Kong, J., Zeng, X. and Ren, J. 2008. Plant species identification based on neural network. Proceedings of the Fourth International Conference on Natural Computation. Oct. 18-20. Jinan, China.
 
Zheng, L., Zhang, J. and Wang, Q. 2009. Mean-shift-based color segmentation of images containing green vegetation. Comput. Electron. Agric. 65, 93-98.
 
Zheng, L., Shi, D. and Zhang, J. 2010. Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminate. Pattern Recogn. Lett. 31(9): 920-925.