DEEP LEARNING ALGORITHM INVENTORY FOR SUGAR CANE LEAF DISEASE DETECTION (Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU), 2025)
Sugarcane (Saccharum spp.) is a major industrial crop whose productivity is highly affected by leaf diseases, making early and precise detection essential for sustainable production. Deep learning offers an advanced, non-destructive solution for disease diagnosis through automated image-based analysis. However, existing studies on sugarcane disease detection remain fragmented, with variations in datasets, preprocessing methods, and evaluation metrics that hinder direct algorithmic comparison. This study systematically inventories and evaluates deep learning algorithms applied to sugarcane leaf disease detection, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid Transformer architectures. A comparative synthesis of widely used pretrained models such as ResNet, EfficientNet, DenseNet, and MobileNet is conducted to identify their relative performance and adaptability to field and UAV imagery. The research also proposes standardised methodological recommendations for preprocessing, augmentation, and evaluation to enhance reproducibility and field-level deployment. The findings bridge the gap between theoretical advances and practical applications in precision agriculture, providing a reference framework for researchers and practitioners to select the most suitable deep learning approaches for real-world sugarcane health monitoring.