Zea Mays Leaf Disease Classification Using Swin Transformer

Agricultural losses have an effect on the global economy, and plant diseases are a primary cause of such losses. To tackle these obstacles, utilizing artificial intelligence (AI) methods can be beneficial in managing the outcomes. Corn is one of the most significant agricultural products. A disease outbreak might cause a significant drop in corn production, resulting in millions of dollars in losses. The risk of crop failure due to a disease pandemic can be mitigated with the help of deep learning (DL) methods. Traditionally, plant illnesses are examined using just one’s own eyes, with the emphasis typically being on color changes, the presence of spots or rotten regions in the leaves, or both. Because of their tiny nature, the symptoms of plant diseases might be difficult for farmers to accurately detect in certain cases. In addition, many people who operate farms are not specialists in the recognition and classification of diseases; thus, vision-based DL methods may be able to aid farmers in delivering more accurate diagnoses, identification, and classification. The CNNs have emerged as the top choice for image processing over the last few years as advancements have been made in related domains. Swin Transformer (Swin-T), which was recently introduced, has shown significant improvement in classification applications. We used the Swin-T to classify maize leaf diseases including as blight, common rust, and gray leaf spot. When compared to currently available approaches, the proposed method demonstrates the highest accuracy of 95.9%.