Enhancing Flower Classification Accuracy: A Case Study of MobileNetV2 vs. ResNet50

In this work, utilizing the ResNet50 deep learning model, offer a complete method for flower categorization with remarkable accuracy of 94.65%. Evaluated on the same dataset, this performance exceeds that of the MobileNetV2 model, which had a reduced accuracy. Our dataset comprises of five different flower classes, which presents a difficult but doable challenge for model validation. ResNet50's deep residual learning architecture helps it to efficiently collect complex characteristics and patterns in the flower photos, hence improving classification accuracy. The model's resilience and efficiency in addressing classification problems are shown by its capacity to sustain great accuracy while controlling challenging visual data. On this particular application, MobileNetV2 falls short in performance even if it is a lightweight and efficient model when compared to ResNet50. ResNet50 is a useful tool for applications in biodiversity monitoring, agricultural management, and ecological research since its notable accuracy shows the potential of it for exact floral classification. Our results highlight the need of model selection in reaching high accuracy in image classification tasks and imply that ResNet50 is a favored choice for detailed and accurate floral classification tasks.