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.
