CTNet: A cognitive-inspired TriPercept CrossFusion self-distillation model for image classification in agricultural field

In agricultural artificial intelligence image classification tasks, existing models often exhibit redundant feature extraction and limited generalization capability. To address these practical demands and technical challenges, we propose a Cognitive-Inspired TriPercept CrossFusion Self-Distillation Network (CTNet). First, to counteract the adverse effects of environmental variations and motion blur inherent in agricultural image acquisition, we introduce a comprehensive data augmentation strategy specifically designed for agricultural imagery, thereby enhancing model robustness. Second, we present an efficient and lightweight TriPercept CrossFusion Network (TPCF), which concurrently captures multi-branch representations and emphasizes discriminative local features. A cross-branch fusion mechanism facilitates inter-branch feature interaction, enabling effective feature learning with minimal computational overhead. Third, we devise a novel end-to-end trainable self-distillation framework, termed Cognitive-Inspired Multi-Stage Self-Distillation (CISD). CISD operates without external pretrained teacher models; instead, it performs intra-model knowledge transfer across layers and employs a composite objective combining classification loss (cross-entropy), distillation loss (KL divergence), and hint loss (feature map alignment). Inspired by cognitive load theory, the framework dynamically adjusts the weighting of these loss components to achieve cognitive-load-aware multi-stage self-distillation, thereby improving generalization. We evaluated CTNet on a custom lychee disease dataset and six public benchmarks. On our augmented lychee dataset, CTNet achieves 99.03% accuracy. On three normal-collected datasets (PlantVillage, CottonWeedID15, and PotatoLeafDisease) the model attains accuracies of 99.74%, 91.81%, and 99.52%, respectively. On three controlled-collected datasets (PlantSeedling, RiceImage, and SoyBeanSeed) the corresponding accuracies are 97.45%, 99.97%, and 93.08%. CTNet consistently outperforms state-of-the-art classification methods across all benchmarks, demonstrating its effectiveness as a practical solution for agricultural image classification tasks.