Multi-Platform Detection of Melon Leaf Abnormalities Using AVGHEQ and YOLOv7

Sahrial Ihsani Ishak; Karlisa Priandana; Sri Wahjuni

doi:10.15575/join.v10i1.1441

Authors

Sahrial Ihsani Ishak School of Data Science, Mathematics and Informatics, IPB University, Bogor, Indonesia https://orcid.org/0000-0002-7314-9610
Karlisa Priandana School of Data Science, Mathematics and Informatics, IPB University, Bogor, Indonesia
Sri Wahjuni School of Data Science, Mathematics and Informatics, IPB University, Bogor, Indonesia

DOI:

https://doi.org/10.15575/join.v10i1.1441

Keywords:

AVGHEQ, Internet of Things, Melon Disease Detection, Multiplatform System, YOLOv7

Abstract

This research develops a multiplatform system for detecting abnormalities in melon leaves, integrating an Internet of Things (IoT) approach using Jetson Nano, a Streamlit-based website, and a mobile application for real-time monitoring. The system employs preprocessing with Average Histogram Equalization (AVGHEQ) to enhance image quality, followed by modeling with the YOLOv7 algorithm on a dataset of 469 training images and 52 test images, validated through 5-fold cross-validation. The model achieved a mean Average Precision (mAP) of 84% with an inference detection time of 4.5 milliseconds. Implementation on Jetson Nano resulted in a 25% increase in CPU usage (from 25% to 50%) and a 20% increase in RAM usage (from 70% to 90%). By combining these platforms and leveraging robust data preprocessing and modeling techniques, the system provides an accessible, efficient, and scalable solution for agricultural monitoring, enabling farmers to address plant health issues promptly and effectively.

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