Watermelon Leaf Disease Detection Using Machine Learning and Deep Learning Based Hybrid Approach

Abstract

Watermelon diseases significantly affect agricultural productivity, leading to economic losses and reduced crop quality. Traditional manual inspection methods are time-consuming, labor-intensive, and susceptible to human error. This research explores various Machine Learning (ML) and Deep Learning (DL) approaches to classify watermelon leaf diseases, with an emphasis on identifying the best-performing models for integration into a hybrid classification system. A dataset of approximately 5000 images, including healthy and diseased leaf samples, was sourced from Kaggle and preprocessed to ensure robust training. Initially, ML algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Random Forest were tested, yielding accuracies ranging from 59% to 88%. DL architectures, including baseline CNN, ResNet50, MobileNetV2, DenseNet121, and InceptionV3, were subsequently evaluated, with ResNet50 achieving the highest accuracy of 99.75%, closely followed by MobileNetV2 with 99%. Based on these findings, a hybrid model was constructed by combining SVM (for classification) and a pre-trained ResNet50 (for feature extraction), achieving an accuracy of 99.80%. This study demonstrates how artificial intelligence can be used practically to advance precision farming by promoting sustainable agricultural practices. By integrating ML and DL techniques into a hybrid model, this research contributes a significant step toward more accurate and impactful solutions for watermelon leaf disease classification, supporting sustainable agriculture and global food security.