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طراحی، ساخت و ارزیابی سامانه سمپاش خودکار به منظور تشخیص برخط علفهرز-گیاه در مزارع چغندرقند | ||
| چغندرقند | ||
| مقاله 8، دوره 37، شماره 2، مهر 1400، صفحه 223-238 اصل مقاله (1.01 M) | ||
| نوع مقاله: کامل علمی - پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/jsb.2022.355302.1298 | ||
| نویسندگان | ||
| هادی اورک1؛ سامان آبدانان مهدی زاده* 2؛ محمدامین آسودار3؛ الهام الهی فرد4 | ||
| 1کارشناسی ارشد پردازش تصویر، دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان. اهواز، ایران. | ||
| 2دانشیار دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان. اهواز، ایران. | ||
| 3استاد دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، اهواز، ایران | ||
| 4استادیار دانشکده کشاورزی، گروه زراعت، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، اهواز، ایران | ||
| چکیده | ||
| کنترل علفهای هرز در دوره رشد گیاهان بسیار مورد توجه بوده و روشهای مختلفی بدین منظور توسعه یافته است. در مبارزه با این گیاهان ناخواسته با روش مرسوم تمام مزرعه و گیاه اصلی نیز مورد حمله علفکش قرار میگیرد که سبب مصرف بیرویه سموم نیز میشود. در این پژوهش یک سامانه سمپاش هوشمند بهمنظور تشخیص علفهرز و بررسی میزان کاهش مصرف سم، در مزرعه چغندرقند بر اساس فناوری بینایی ماشین ((Machine Vision ارائه شد. به این منظور 49 ویژگیهای ظاهری و رنگی چغندرقند و علفهرز از تصاویر استخراج و مورد بررسی قرار گرفتند. با پیادهسازی الگوریتم ژنتیک (GA) 11 ویژگی که بیشترین دقت در تشخیص را داشتند انتخاب و به منظور افزایش سرعت و بهترین عملکرد پنج ویژگی (ضریب شعاع ناحیه محدب هال، ضریب کرویت، ممان ششم، I1I2I3_I3 و Lch_c) که بیشترین تکرار را در انتخاب ویژگی داشتند، برگزیده شدند. الگوریتم توسعه یافته برای تشخیص علف هرز از چغندرقند، دقت بیش از 98 درصد داشت که نشان از قدرت تشخیص بالای این سامانه هوشمند میباشد. جهت بررسی میزان کاهش مصرف محلول سم، سامانه سمپاش هوشمند با سمپاش بافرآگری ((Buferagri مورد مقایسه قرار گرفت. سرعت حرکت هشت کیلومتر بر ساعت با تراکتور نوع جاندایر و مسافت پیموده شده 27 متر برای هر دو سمپاش ثابت در نظر گرفته شد. در یک مسافت مشخص میزان علفکش مصرفی اندازهگیری و در نهایت میزان مصرف محلول توسط سمپاش بافرآگری نسبت به سامانه سمپاش هوشمند بیش از 77 درصد بود. این مساله نشان از کارایی این سامانه نسبت به سمپاشهای معمولی در کاهش مصرف علفکش است. نتایج نشان دادند که استفاده از سامانه ارائه شده به صورت سیستم سمپاش هوشمند در مزارع چغندرقند امکانپذیر است. | ||
| کلیدواژهها | ||
| الگوریتم ژنتیک؛ بینایی ماشین؛ پردازش تصویر؛ چغندرقند؛ سمپاش هوشمند؛ کاهش مصرف سم | ||
| عنوان مقاله [English] | ||
| Design, construction and evaluation of an automatic sprayer system online weed-plant detection in sugar beet fields | ||
| نویسندگان [English] | ||
| H. Orak1؛ S. Abdanan Mehdizadeh2؛ M.A. Asoodar3؛ E. ٍElahifard4 | ||
| 1Master of Science in Biosystems engineering of Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Resources University of Khuzestan, Ahvaz, Iran. | ||
| 2Associate Professor, Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Resources University of Khuzestan, Ahvaz, Iran. | ||
| 3Professor of Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Resources University of Khuzestan, Ahvaz, Iran. | ||
| 4Assistant Professor, Department of Agronomy, Faculty of Agricultural, Agricultural Sciences and Resources University of Khuzestan, Ahvaz, Iran. | ||
| چکیده [English] | ||
| Weed control during the growth period of plantsis of great importance and various methods have been developed for this purpose. In the fight against these unwanted plants with the conventional method, the entire field as well as the main plant are covered by herbicides, which also causes excessive application of herbicide. In this study, a smart sprayer system was used to detect weed and reduce the amount of herbicide application in sugar beet field based on machine vision technology. For this purpose, 49 phenotype and color characteristics of beet and weed images were extracted and analyzed. By implementing Genetic Algorithm (GA), 11 characteristics that were most accurate in diagnosis were selected, and in order to increase the speed and improve performance, five characteristics (area ratio of convex hull, sphericity coefficient, moment 6, I1I2I3 and Lch_c) with had the most replication number in characteristic selection were selected. The developed algorithm to distinguish weed from sugar beet had an accuracy of more than 98%, which shows the high detection power of this smart system. To evaluate the reduction of herbicide usage, the smart sprayer system was compared with the Buferagri sprayer. The movement speed of 8 km hr-1 with John Deere tractor and 27 m travelled distance were considered to be fixed for both sprayers. In a certain distance, the amount of herbicide used was measured and it could be observed that the amount of herbicide used in the Buferagri sprayer was 77% higher than the smart sprayer system. This shows the efficiency of this system compared with conventional sprayers in reducing herbicides application. These results indicate the feasibility of using the presented system as a smart sprayer system in sugar beet fields. | ||
| کلیدواژهها [English] | ||
| Genetic Algorithm, Herbicide reduction Weed, Image processing, Smart sprayer, Sugar beet | ||
| مراجع | ||
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