Knowledge distillation-based image deblurring network for plant disease classification

The accuracy of camera-based plant disease classification is crucial for smart farming applications, particularly in automated management systems using agricultural robots. To address the performance degradation caused by motion blur, we propose a knowledge distillation (KD)-based image deblurring network (KDID-Net). Our framework targets classification performance by integrating generative adversarial network (GAN)-based deblurring with fast Fourier transform (FFT)-based complex-domain KD. Specifically, the teacher model in KDID-Net employs a classification-guided (CG) block that generates logits analogous to those produced by a classifier, which are then used to compute a classification-guided enhancement loss (CGEL), thereby reinforcing features relevant to disease classification. Furthermore, the teacher network’s feature maps are subjected to FFT, and frequency-domain knowledge is distilled into a lightweight student network, enabling the recovery of fine lesion details. Consequently, the lightweight network achieves high classification accuracy while remaining suitable for agricultural robots with limited computational resources. Moreover, the disease classification results produced by the proposed method are automatically linked to the input of the open-source large language model Meta artificial intelligence (LLaMA), enabling a system that recommends optimal pesticide information for each disease class. We conducted experiments with two public datasets: the PlantVillage and FieldPlant datasets. Under the same blur conditions, KDID-Net achieved classification accuracies of 0.9432 and 0.7571, respectively, outperforming state-of-the-art (SOTA) image restoration methods and KD methods in terms of classification accuracy. These results demonstrate the effectiveness of the proposed method for plant disease classification under both simple and complex background conditions. Furthermore, the proposed model achieved superior performance compared with SOTA methods in terms of graphic processing unit (GPU) memory requirements, floating point operations (FLOPs), and inference times on computationally constrained embedded systems, demonstrating its suitability for agricultural robot applications.