تشخیص خودکار ردیف‌های کشت گیاه توسط ربات به کمک بینایی ماشین (مطالعه موردی: گیاه ریحان)

در کشاورزی دقیق، مکان‌یابی خودکار محصولات یکی از موضوعات تحقیقاتی مهم است. در این پژوهش شناسایی دقیق ردیف‌های کشت محصول ریحان به‌منظور مسیریابی خودکار یک ربات گزارش می‌شود. در یک مرحله از این تحقیق با اخذ تعداد شش تصویر در هر دوره‌ی رشد (هفته سوم، هفته چهارم و هفته پنجم) به حذف علف‌های هرز میان ردیف‌های کشت محصول پرداخته شد؛ که بدین منظور از سه روش مختلف (گشودگی مساحت، حذف ابعادی و ماسک‌گذاری) استفاده گردید. در مرحله‌ای دیگر تعداد شش تصویر از ردیف‌های کشت بدون وجود علف‌های هرز مورد بررسی قرار گرفت. سپس با انجام عملیات پردازش تصویر و پیاده‌سازی چندین الگوریتم مسیریابی (الگوریتم‌های مبتنی بر تبدیل هاف، تبدیل موجک، فیلتر گابور، رگرسیون خطی و الگوریتم پیشنهادی این مطالعه) بر روی تصاویر، به بررسی خروجی هر یک از این الگوریتم‌ها نسبت به مسیر ایده‌آل تعیین‌شده توسط کاربر پرداخته شد. پس از مقایسه‌ی دقیق مسیرهای تشخیص داده شده توسط الگوریتم‌های مختلف مسیریابی نسبت به مسیرهای ایده‌آل و با توجه به نتایج آزمون آماری t-test در سطح احتمال 5%، برتری روش‌های مسیریابی مورد مطالعه به‌ترتیب زیر مشخص گردید: روش پیشنهادی، روش فیلتر گابور، روش رگرسیون خطی، روش تبدیل هاف و روش تبدیل موجک. در نهایت الگوریتم پیشنهادی با بیشترین میزان انطباق نسبت به مسیر ایده‌آل (با میانگین خطای تشخیص 3.65 پیکسل) و کمترین مدت زمان اجرای عملیات (4.79 ثانیه) به‌عنوان مناسب‌ترین الگوریتم مسیریابی انتخاب و با استفاده از آن عملکرد یک ربات طراحی‌شده مورد ارزیابی قرار گرفت.

automatic detection of plant cultivation rows robot using machine vision (case study: basil plant)

introductionnowadays, machine vision systems are extensively used in agriculture. the application of this technology in the field can help preserve agricultural resources while reducing manual labor and production costs. in the field of agricultural automation, accurately detecting crop rows is recognized as a crucial and challenging issue for weed identification and the automatic guidance of machines. therefore, it is necessary to explore practical solutions to optimize this process. hence, the purpose of this study is the precise identification of basil cultivation rows to enable the automatic navigation of robots in the cultivation field.materials and methodsin the first stage of this research, six images from each growth period of basil plants (third, fourth, and fifth week) were taken and weeds were removed from the area between the crop rows using three different methods of area opening, dimensional removal, and masking. in the next stage, six images of crop rows without weeds were examined by performing image processing operations and implementing several routing algorithms, namely, hough transform, wavelet transform, gabor filter, linear regression, and an additional algorithm proposed in this study. the output of each of these algorithms was compared with the ideal path identified by the user. for this purpose, after capturing an image, green areas were extracted from it by performing the segmentation process. by applying each of the routing algorithms to the image, plant cultivation lines were identified and their equations were determined. finally, the performance of the designed robot was evaluated using the most appropriate routing algorithm.results and discussionexamining the performance of three different methods of weed removal in three periods of plant growth (third, fourth, and fifth week) showed that during this interval, the masking method had the lowest error rate compared to the ideal path and the shortest average operation time of 1.64 seconds, followed by the dimensional removal and the area opening methods. comparing the routes detected by different routing algorithms with the ideal routes and according to the results of the t-test at 5% probability level, the order of the studied routing methods from the most superior is as follows: the proposed algorithm, gabor filter, linear regression, hough transform and wavelet transform algorithm. overall, the proposed algorithm had the highest rate of adaptation to the ideal path (with an average error of 3.65 pixels) and the shortest operation time (4.79 seconds) and was selected as the most appropriate routing algorithm and the performance of the designed robot was evaluated using it.conclusiona reliable crop row detection algorithm can reduce production costs and preserve the environment. in this study, the masking method was used for removing weeds from the images. the new proposed routing algorithm has superior performance when compared with common routing algorithms such as the gabor filter, linear regression, hough transform, and wavelet transform. additionally, it was shown that the designed robot using the proposed algorithm (with an average error of 3.65 pixels) has the desired performance.acknowledgmentthe authors express appreciation for the financial support provided by tarbiat modares university.