Non-destructive classification of sugarcane milling feasibility using deep learning: A comparative study of VGG19 and ResNet50 - Nur Indrianti; Raden Achmad Chairdino Leuveano; Heru Cahya Rustamaji; Andrey Ferriyan; Panut Mulyono; Bayu Prasetya Wijaya (International Journal of Advances in Intelligent Informatics, 2026)
Assessing sugarcane quality is crucial for ensuring both economic value and processing efficiency in sugar production. Conventional approaches, such as refractometer-based Brix measurements, are destructive, labor-intensive, and unsuitable for large-scale or rapid field evaluations. This study proposes a non-destructive deep learning framework for classifying sugarcane internodes into two quality categories (< 16 °Bx and ≥16 °Bx) to address existing limitations. Two convolutional neural network architectures, VGG19 and ResNet50, were evaluated utilizing a defined transfer learning and data augmentation methodology. Because of its residual connections, which enable deeper and more stable feature learning, ResNet50 consistently outperformed VGG19, achieving the highest accuracy of 78.85% on the Luar2_Putih dataset. This comparative finding demonstrates that modern residual-based networks provide superior robustness for subtle visual classification tasks in agricultural imaging, while also validating the stability of the proposed two-phase training framework. The study advances AI-driven non-destructive quality assessment by offering a scalable, field-deployable solution that supports sustainable, efficient sugarcane processing in line with the UN Sustainable Development Goals (SDG 2, 9, 12, and 13).