The following limitations exist in the traditional crop stress detection techniques in the background of smart farming applications: low detection accuracy, inefficient feature extraction, poor adaptability to the environment, and high computational complexity. Stress detection using traditional machine learning and convolution-based methods is inefficient in capturing complex stress patterns of drought, pests, diseases, and nutrient deficiency, affecting productivity and precision agriculture systems. To overcome these challenges, a novel self-supervised vision Transformer with swarm intelligence (SSVT-SI) based efficient and pattern-aware crop stress detection model is introduced. The proposed method leverages self-supervised learning for meaningful representation learning from unlabeled agricultural images and applies a Vision Transformer for long-range spatial relationships and hidden stress patterns in crops. Furthermore, to optimize feature selection, and to enhance the classification performance in low computational cost, Swarm Intelligence optimization is embedded. The model was tested on two sets of rice leaf disease and PlantVillage, and the pictures of healthy plants and stressed plants were taken under different farm conditions. Our experimental results achieve 98.42% accuracy, 97.86% precision, 97.54% recall, 97.70% F1-score and 0.052 loss value when compared with the existing CNN and hybrid deep learning methods. The framework provides for accurate early detection of stress, minimizes manual stress monitoring, supports the smart farming vision of agriculture and enables intelligent farming of crops to ensure sustainable agricultural production.