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ارائه یک مدل مبتنی بر یادگیری عمیق برای مکان‌یابی، تشخیص و طبقه‌بندی علفهای هرز در مزارع سیب‌زمینی | ||
| مدل سازی در مهندسی | ||
| دوره 22، شماره 79، دی 1403، صفحه 79-97 اصل مقاله (1.36 M) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2024.32372.2562 | ||
| نویسندگان | ||
| هلو عمر انور1؛ فردین اخلاقیان طاب1؛ محسن رمضانی* 1؛ کاوه ملازاده2 | ||
| 1گروه مهندسی کامپیوتر، دانشکده مهندسی، دانشگاه کردستان، سنندج، ایران | ||
| 2گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه کردستان، سنندج، ایران | ||
| تاریخ دریافت: 27 آبان 1402، تاریخ بازنگری: 08 خرداد 1403، تاریخ پذیرش: 12 خرداد 1403 | ||
| چکیده | ||
| برداشت محصول سیب‌زمینی در زمین‌های کشاورزی با کمترین مصرف مواد اولیه (علف کشها یا عملیات مکانیکی) یکی از اهداف اصلی کشاورزی پایدار است. در کشاورزی دقیق، سیستم‌های مبتنی بر بینایی ماشین به عنوان حسگرهایی که در حالت حرکت برای تشخیص علف‌ها از گیاه اصلی کاربرد دارند، میتوانند مورد استفاده قرار گیرند. این مقاله یک مدل جدید بر اساس یادگیری عمیق برای تشخیص علفهای هرز در زمین‌های سیب‌زمینی را ارائه می‌کند. در این راستا، ابتدا یک پایگاه داده جامع از زمین تحت کشت سیب‌زمینی شامل تصاویری از مراحل مختلف رشد گیاه، در فواصل مختلف دوربین از زمین، در ساعات مختلف روز و در شرایط محیطی متفاوت تهیه شده است. سپس، با استفاده از الگوریتم عمیق YOLOV3، موقعیت کل گیاهان در زمین تعیین میگردد. در نهایت، برای جداسازی علفهای هرز از گیاه اصلی و تعیین نوع علف هرز سه شبکه‌های عصبی کانولوشنی توسعه داده شده است. نتایج نشان داده که الگوریتم YOLOV3 قادر است به خوبی مکان گیاهان را در تصاویر تشخیص دهد. همچنین این روش میتواند با دقت 99.64٪ علف‌ها را از گیاهان سیب‌زمینی در مجموعه تصاویر آزمایشی تشخیص دهد. نتایج دسته‌بندی 9 گونه مختلف علفهای هرز نیز با استفاده از مدل‌های یادگیری عمیق توسعه داده شده قابل قبول است؛ به طوری که دقت کلی مدل‌های EN-Inception-V3، EN-VGG-16 و HCNN به ترتیب در فاز آموزش 99.82٪، 99.89٪ و 92.83٪ و در فاز آزمایش 96.69٪، 90.32٪ و 82.67٪ میباشد. البته ترکیب این مدلها دقت تشخیص را تا 98.2% ارتقا می دهد. | ||
| کلیدواژه‌ها | ||
| شبکه عصبی کانولوشنی؛ بینایی ماشین؛ کشاورزی دقیق؛ مدل عمیق؛ مدل دوست‌دار محیط | ||
| عنوان مقاله [English] | ||
| A New Deep Vision-Based Identifier As An Intelligent Herbicide Spraying Agent For Potato Farm Application | ||
| نویسندگان [English] | ||
| Halo Omer Anvar1؛ Fardin Akhlaghian Tab1؛ Mohsen Ramezani1؛ Kaveh Mollazade2 | ||
| 1Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran | ||
| 2Department of Biosystems Engineering, University of Kurdistan, Sanandaj, Iran | ||
| چکیده [English] | ||
| Weeding in potato fields with the least consumption of inputs (herbicide or mechanical operations) is one of the main goals of sustainable agriculture. In precision agriculture, machine vision-based systems are used as on-the-go sensing units to detect weeds from the main plant. This paper presents a new approach based on deep learning to detect weeds in potato fields. For this purpose, first, a comprehensive database was created, including the acquired images of the potato field (at different stages of plant growth, at different distances of the camera from the ground, at different hours of the day, and in different environmental conditions). Then, the location of the plants in the field (including weeds and potato plants) was determined using the deep YOLOV3 algorithm. Finally, to separate weeds from the main plant as well as to determine the type of weed, three different types of convolutional neural networks were developed. The results showed that the YOLOV3 algorithm is well able to localize the plants in the images. EN-Inception-V3 classifier was able to distinguish weeds from potato plants in the set of test images with 99.42% accuracy. The classification results of 9 different weed species using the developed deep learning models were satisfactory; so that the overall accuracy of EN-Inception-V3, EN-VGG-16, and HCNN models was 99.82%, 99.89%, and 92.83% in the training phase, and 96.69%, 90.32%, and 82.67% in the test phase, respectively. It should be noted that the combination of models leads to 98.2% accuracy in detecting the type of weeds. | ||
| کلیدواژه‌ها [English] | ||
| Convolutional neural networks, Machine vision, Precision agriculture, Deep model, Environmentally friendly model | ||
| مراجع | ||
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