Maize leaf disease image classification based on ResNet18 image classification model

Abstract

This paper introduces an effective algorithm for classifying maize leaf diseases using a publicly available image dataset. While the dataset itself, consisting of maize leaf disease images, was developed by Indian researchers for agricultural applications, this study's contribution is the development and optimization of an image classification algorithm tailored for this dataset. The paper details the preprocessing and data augmentation techniques applied, such as size adjustment, normalization, and enhancement to ensure data uniformity and diversity. These methods significantly improve the model's accuracy and generalization capabilities. The study employs a deep learning model based on resnet18, dividing the dataset into training, validation, and test sets in a 6:2:2 ratio. The training process is analyzed through loss and accuracy curves, revealing a steady decrease in loss and increase in accuracy over time. The model achieves 98% accuracy, 95% Precision, 95% Recall, and a 95% F1-score, indicating high effectiveness in classifying maize leaf diseases. A confusion matrix analysis confirms the model's precision across most image types. The research demonstrates the algorithm's strong performance in classifying various types of maize leaf diseases, highlighting its potential to aid farmers in disease identification and treatment, thereby improving agricultural productivity and quality.