International Conference on Computer and Knowledge Engineering

Home / 15th International Conference on Computer and Knowledge Engineering

Simulation-Based Data Augmentation for Apple Leaf Disease Using Statistical Moments and HSV Color Features

Authors :

Seyedeh Maryam Moosavi¹ Morteza Gholipour² Yasser Baleghi³

1- Department of Electrical and Computer Engineering Babol Noshirvani University of Technology 2- Department of Electrical and Computer Engineering Babol Noshirvani University of Technology 3- Department of Electrical and Computer Engineering Babol Noshirvani University of Technology

Keywords :

Precision Agriculture،Data Augmentation،Simulation-based Augmentation،Plant Disease

Abstract :

Traditionally, plant disease detection relied on labor-intensive visual inspection. With the rise of precision agriculture, machine learning methods have gained traction for automated detection but require large labeled datasets, which are often difficult to collect due to disease rarity and annotation costs. To overcome this limitation, this study introduces a realistic data augmentation method based on statistical analysis of apple leaf lesions infected by apple scab, using the publicly available PlantVillage dataset. The method reconstructs the color texture of disease lesions by analyzing their statistical and color space properties. Lesion distribution masks are created at various scales to simulate a range of lesion sizes and spatial patterns. Smooth transitions and natural appearances are achieved by gradually blurring lesion boundaries using a Gaussian function. The leaf skeleton is extracted through a median-based approach to place lesions at anatomically plausible locations, resulting in visually realistic synthetic images. These synthetic lesions are then fused with healthy leaf images by pixel-wise multiplication to generate realistic diseased samples. The effectiveness of the proposed approach was evaluated using the SegNet architecture and the F1-score as the evaluation metric. Experimental results show that pretraining the model on synthetic images and fine-tuning with real data improves the F1-score by 25% compared to training solely on real data. This method offers a practical solution to the challenge of data scarcity in plant health monitoring and smart agriculture.

Papers List

List of archived papers

Persian Legal Text Simplification Leveraging Transformer-Based Models

Mohammadreza Joneidi Jafari - Saedeh Tahery - Amirhossein Nikoofard

A Framework for Automated Cardiovascular Magnetic Resonance Image Quality Scoring based on EuroCMR Registry Criteria

Shahabedin Nabavi - Mohsen Ebrahimi Moghaddam - Ahmad Ali Abin - Alejandro Frangi

Improving Soft Error Reliability of FPGA-based Deep Neural Networks with Reduced Approximate TMR

Anahita Hosseinkhani - Behnam Ghavami

Learning to Classify Messier Astronomical Objects with Limited Data: A Few-Shot Learning Approach

AMIRREZA ROUHBAKHSHMEGHRAZI - Shayan Nalbandian - Ghazal Alizadeh - Sheida Shadman - Shuyuan Yang - Bo Li

Security Analysis of MiniApps: Vulnerabilities, Exploits, and a Tailored Mitigation Framework

Keyhan Mohammadi - Arman Moradi - Reza Ebrahimi Atani

Averting Mode Collapse for Generative Zero-Shot Learning

Shayan Ramazi - Setare Shabani

Variance-Guided Feature Correlation for Deep Full-Reference Image Quality Assessment

Amirreza Khakpour - Sina Yademellat - Azadeh Mansouri

Graph Representation Learning Towards Patents Network Analysis

Mohammad Heydari - Babak Teimourpour

Using Deep Learning for Classification of Lung Cancer on CT Images in Ardabil Province

Mohammad Ali Javadzadeh Barzaki - Jafar Abdollahi - Mohammad Negaresh - Maryam Salimi - Hadi Zolfeghari - Mohsen Mohammadi - Asma Salmani - Rona Jannati - Firouz Amani

Generating Hand-Written Symbols With Trajectory Planning Using A Robotic Arm

Arya Parvizi - Armin Salimi-Badr