Abstract
We present a comprehensive dataset of 5,323 images of mint (pudina) leaves in various conditions, including dried, fresh, and spoiled. The dataset is designed to facilitate research in the domain of condition analysis and machine learning applications for leaf quality assessment. Each category of the dataset contains a diverse range of images captured under controlled conditions, ensuring variations in lighting, background, and leaf orientation. The dataset also includes manual annotations for each image, which categorize them into the respective conditions. This dataset has the potential to be used to train and evaluate machine learning algorithms and computer vision models for accurate discernment of the condition of mint leaves. This could enable rapid quality assessment and decision-making in various industries, such as agriculture, food preservation, and pharmaceuticals. We invite researchers to explore innovative approaches to advance the field of leaf quality assessment and contribute to the development of reliable automated systems using our dataset and its associated annotations.