Accurate and timely identification of diseases and pests impacting rice cultivation is critical for farmers to swiftly intervene and minimize economic losses. While recent advancements in convolutional neural networks (CNNs) have boosted image classification accuracy, their resource-intensive nature necessitates leveraging pre-trained models