The accurate segmentation of significant rice diseases and assessment of the degree of disease damage are the keys to their early diagnosis and intelligent monitoring and are the core of accurate pest control and information management. Deep learning applied to rice disease detection and segmentation can significantly improve the accuracy of disease detection and identification but requires a large number of training samples to determine the optimal parameters of the model. This study proposed a lightweight network based on copy paste and semantic segmentation for accurate disease region segmentation and severity assessment. First, a dataset for rice significant disease segmentation was selected and collated based on 3 open-source datasets, containing 450 sample images belonging to 3 categories of rice leaf bacterial blight, blast and brown spot. Then, to increase the diversity of samples, a data augmentation method, rice leaf disease copy paste (RLDCP), was proposed that expanded the collected disease samples with the concept of copy and paste. The new RSegformer model was then trained by replacing the new backbone network with the lightweight semantic segmentation network Segformer, combining the attention mechanism and changing the upsampling operator, so that the model could better balance local and global information, speed up the training process and reduce the degree of overfitting of the network. The results show that RLDCP could effectively improve the accuracy and generalisation performance of the semantic segmentation model compared with traditional data augmentation methods and could improve the MIoU of the semantic segmentation model by about 5% with a dataset only twice the size. RSegformer can achieve an 85.38% MIoU at a model size of 14.36 M. The method proposed in this paper can quickly, easily and accurately identify disease occurrence areas, their species and the degree of disease damage, providing a reference for timely and effective rice disease control.

The advancement of smart agriculture, with information technology serving as a pivotal enabling factor, plays a crucial role in achieving food security, optimizing production efficiency, and preserving the environment. Simultaneously, wireless communication technology holds a critical function within the context of applying the Internet of Things in agriculture. In this research endeavor, we present an algorithm for lightweight channel authentication based on frequency-domain feature extraction. This algorithm aims to distinguish between authentic transmitters and unauthorized ones in the wireless communication context of a representative agricultural setting. To accomplish this, we compiled a dataset comprising legitimate and illegitimate communication channels observed in both indoor and outdoor scenarios, which are typical in the context of smart agriculture. Leveraging its exceptional perceptual capabilities and advantages in parallel computing, the Transformer has injected fresh vitality into the realm of signal processing. Consequently, we opted for the lightweight MobileViT as our foundational model and designed a frequency-domain feature extraction module to augment MobileViT’s capabilities in signal processing. During the validation phase, we conducted a side-by-side comparison with currently outstanding ViT models in terms of convergence speed, precision, and performance parameters. Our model emerged as the frontrunner across all aspects, with FDFE-MobileViT achieving precision, recall, and F-score rates of 96.6%, 95.6%, and 96.1%, respectively. Additionally, the model maintains a compact size of 4.04 MB. Through comprehensive experiments, our proposed method was rigorously verified as a lighter, more efficient, and more accurate solution.