การตรวจจับความผิดปกติของใบหญ้าเอ็นยืด (Plantago majorjor L.)  ในการเพาะปลูกด้วยการประมวลผลภาพ

Anusorn Yodjaiphet; Praphas Suwan; Thak Hongthong; Prakasit  Sritakaew; Kanchana  Boontasri; Wichet  Thipprasert

doi:10.57260/stc.2026.1118

Authors

Anusorn Yodjaiphet Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai
Praphas Suwan Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai
Thak Hongthong Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai
Prakasit Sritakaew Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai
Kanchana Boontasri Faculty of Science, Rajamangala University of Technology Lanna, Chiang Rai
Wichet Thipprasert Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai

DOI:

https://doi.org/10.57260/stc.2026.1118

Keywords:

Broadleaf plantain, Plantago major L., YOLOv8, Image processing, Plant leaf abnormality detection, Web application

Abstract

This study presents a method for detecting leaf abnormalities in cultivated broadleaf plantain (Plantago major L.) by integrating image processing with deep learning. The workflow includes image acquisition from real cultivation environments, image enhancement and dataset preparation, annotation, and training of a YOLOv8 object detection model to detect and classify leaf abnormalities into three categories: yellowing leaves, insect-damaged leaves, and perforated leaves. Additionally, a prototype web application was developed and implemented to support image submission and display detection results in near real-time. Experimental results demonstrate that the proposed model can effectively classify leaf abnormalities, achieving a mean Average Precision (mAP) at 0.5 of 0.95. The proposed approach has the potential to support plant health monitoring and reduce the need for labor-intensive manual inspections. However, the system output is intended for symptom-level screening of visible leaf abnormalities and is not designed to confirm plant diseases or diagnose the underlying causes of such abnormalities.

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