طراحی، ساخت و ارزیابی سامانه سمپاش خودکار به منظور تشخیص برخط علف‌هرز-گیاه در مزارع چغندرقند

نوع مقاله : کامل علمی - پژوهشی

نویسندگان

1 کارشناسی ارشد پردازش تصویر، دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان. اهواز، ایران.

2 دانشیار دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان. اهواز، ایران.

3 استاد دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، اهواز، ایران

4 استادیار 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

عنوان مقاله [English]

Design, construction and evaluation of an automatic sprayer system online weed-plant detection in sugar beet fields

نویسندگان [English]

  • H. Orak 1
  • S. Abdanan Mehdizadeh 2
  • M.A. Asoodar 3
  • E. ٍElahifard 4
1 Master 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.
2 Associate Professor, Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Resources University of Khuzestan, Ahvaz, Iran.
3 Professor of Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Resources University of Khuzestan, Ahvaz, Iran.
4 Assistant 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

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چغندرقند
دوره 37، شماره 2
مهر 1400
صفحه 223-238

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