Bibliographic Details
| Title: |
Detecting Defect in Central Pivot Irrigation System Using YOLOv5 Algorithms. |
| Authors: |
N. Hijab, Omar1,2, T. Al-Qaysi, Z.1 ziadoontareq@tu.edu.iq, Ahmed, M. A.1, Salih, Mahmood M.1, Shuwandy, Moceheb L.1, Abdulateef, Salwa K.1 |
| Source: |
Iraqi Journal for Electrical & Electronic Engineering. Jun2026, Vol. 22 Issue 1, p24-35. 12p. |
| Subjects: |
Center pivot irrigation, Object recognition (Computer vision), Real-time computing, Image processing, Fault diagnosis, Machine learning, Agricultural technology, Precision farming |
| Abstract (English): |
Global agriculture employs central pivot irrigation system(CPIS) as a highly significant method for intelligent irrigation. Cultivating crucial crops like wheat and other strategically important crops that occupy extensive land areas contributes to global food security. The Central Pivot Irrigation System encounters technical issues that result in malfunctions in its automatic control system. These malfunctions occasionally cause damage to the primary pipes and towers that operate the system, resulting in significant material losses for farmers and agricultural crops. Moreover, the repair process is time-consuming. Therefore, to address this issue, this study employed the YOLOv5 models to accurately identify and detect defects in the CPIS machine by determining whether they are in a safe or dangerous state. The dataset that was used in this study was gathered from agricultural areas in Salah al-Din Governorate. The CPIS detection model yielded the following results: the grayscale color system with Yolov5n achieved a 98 % detection rate with accuracy and F1-score values of 0.866. Similarly, Yolov5m achieved a 98 % detection rate with accuracy and F1-score values of 0.804. In the RGB color system, the maximum results achieved with Yolov5n are 97 % for accuracy and 0.812 for F1-score. On the other hand, Yolov5s6 achieves a result of 95 % for accuracy and 0.82 for both F1-score and accuracy. Based on the aforementioned outcome, we can infer that yolov5s6 accurately detects the CPIS in both its safe and dangerous states. Therefore, they can be deployed in a real-time system for CPIS defect monitoring and control systems. [ABSTRACT FROM AUTHOR] |
| Abstract (Arabic): |
تركز هذه المقالة على تطوير نموذج لاكتشاف العيوب في نظام الري المحوري المركزي (Central Pivot Irrigation System - CPIS) باستخدام خوارزمية YOLOv5، وهي خوارزمية تعتمد على التعلم العميق للكشف عن الأشياء. يُستخدم نظام الري المحوري المركزي على نطاق واسع في الزراعة الذكية العالمية لمحاصيل مثل القمح، لكنه يواجه أعطالًا تقنية قد تتلف مكوناته وتتسبب في خسائر كبيرة. جمعت الدراسة مجموعة بيانات تضم 2800 صورة (لحالات آمنة وحالات بها عيوب) من محافظة صلاح الدين في العراق، وتم تدريب عدة نسخ من نموذج YOLOv5 على صور ملونة (RGB) وأخرى بتدرجات الرمادي. أظهرت النتائج أن نموذج YOLOv5n مع صور بتدرجات الرمادي حقق أعلى دقة في الكشف بلغت 98% ودرجة F1 بلغت 0.866، متفوقًا على النماذج الأخرى وأنظمة الألوان المختلفة. تشير النتائج إلى أن نماذج YOLOv5، وخاصة مع إدخال صور بتدرجات الرمادي، يمكنها اكتشاف عيوب نظام الري المحوري المركزي بفعالية وكفاءة في الوقت الحقيقي، مما يفتح إمكانية تطبيقها في أنظمة المراقبة والتحكم الآلية. [Extracted from the article] |
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| Database: |
Engineering Source |
