Plant Disease Recognition Using Residual Convolutional Enlightened Swin Transformer Networks

Kumar et al. introduced a framework, gCrop, that uses system learning and computer vision approaches to estimate leaf development in nurseries or herbal environments. The framework has shown high accuracy levels of 98-100% in determining the aspect and lifespan of leaves. Maniyath’s investigation focuses on using AI models, specifically a Random forest classifier, to identify flora anomalies in nurseries. The model achieved a 70% accuracy prediction and can be improved with more training data and the use of local additives.