banana_ripening_dataset_day0_to_day7

This dataset presents a structured time-series collection of banana images captured over a period of eight consecutive days (Day 0 to Day 7) to analyze the visual progression of fruit ripening. The dataset was developed as part of a research internship project focused on applying computer vision and machine learning techniques to agricultural and food quality assessment problems.

The dataset consists of approximately 30–35 individual banana samples, where each sample is uniquely identified and stored in separate folders (Banana_ID_xxx). Each folder contains a sequence of images (day0 to day7) representing the same banana observed daily under controlled and consistent environmental conditions. This organization enables longitudinal tracking of physical and color changes associated with the ripening process.

The dataset is suitable for a wide range of applications, including image classification, ripeness stage detection, object tracking, and time-series analysis. It can be used to train and evaluate machine learning and deep learning models for tasks such as automated fruit grading, quality inspection, and predictive modeling of ripening stages.

All images are captured with consistent background settings to minimize noise and ensure reliable feature extraction. The dataset structure supports both per-sample temporal analysis and cross-sample comparative studies.

This dataset can serve as a benchmark resource for researchers and practitioners working in computer vision, precision agriculture, and food technology domains.