The Lightweight Swin Transformer for Salinity Degree Classification in a Natural Saline Soil Environment

Excessive salt in soil can significantly reduce crop yield and quality by hindering nutrient absorption. Accurate classification of soil salinization degree is very important for the development of effective management strategies. In this paper, we propose a novel deep learning-based method to identify the degree of soil salinization using a Swin Transformer model enhanced with token-based knowledge distillation. The Swin Transformer, as the backbone of the model, provides comprehensive contextual information and a larger receptive field, ensuring efficient feature extraction. By incorporating token-based distillation, we effectively reduce model size and inference time, overcoming the traditional challenges of large parameters in Transformer models. Our model achieves a classification accuracy of 96.3% on the saline soil datasets of three degrees categories, outperforming existing methods. Compared to the baseline model, the number of parameters is reduced by 80.8%, ensuring faster and more efficient salinity detection. This method not only enhances the accuracy of soil salinity classification but also offers a cost-effective solution, providing valuable data to guide agricultural practitioners in improving soil quality and optimizing land resource management.