Self-Supervised Domain-Adaptive learning for Self-Labeling unknown rice grains during actual rice transportation process

Abstract

Industrial rice agriculture has transitioned towards intelligent computing, with an increasing likelihood of incorporating unmanned decision-making systems. However, one lingering issue that persists is the continued reliance on annotations for activity description. Applying transfer learning and domain-adaptive learning to non-labeled target data with the assistance of source data can be explicitly, but depends on mitigating domain discrepancy instead of principally generating predictive. This compromise both target robustness and comprehensibility. Our aim to enable a model to rely effectively on explicit source knowledge and self-labeling to reliably self-annotate. Our proposed self-supervised domain adaptation method provides debiasing by utilizing both former and present states, which reduces inconsistency when generating imbalanced pseudo-labels and implicitly utilizes source knowledge to consistently increase target-specific knowledge. Comprehensive results show improved performance by 7.5% for public data benchmarks and a gain of 2.4% for a rice transportation scenario. Ablation studies reveal improvements of up to 8.2% in Top-1 accuracy and 2.5% in Top-5 accuracy compared to an existing debiasing method, and highlights substantial parameter and FLOPs saving of up to 4.2 and 14 times, respectively. This underscores the feasibility of our system over self-label-dependent learning.