Addressing Class Imbalance in Rice Disease Diagnosis with Contrastive Dissimilarity

Rice disease recognition is an important task in agricultural diagnostics, as it directly affects crop health and yield. The challenge intensifies due to the visual similarity between symptoms of different diseases and pests, as well as the inherent class imbalance within real-world datasets, where some conditions are more represented than others. In this paper, we explore the application of contrastive dissimilarity for rice disease identification using the Paddy Doctor dataset. Our approach specifically targets the challenges of uneven class distribution, aiming to improve recognition accuracy in data-scarce scenarios common in precision agriculture. Through an extensive set of experiments, we demonstrate that our approach generates robust and discriminative representations, even under conditions of imbalanced and/or underrepresented data, reaching 98.4% on accuracy.