Dataset for digital images of Good and bad classes of Lakshmanbhog Mango (Mangifera indica) slice

This dataset contains digital images of fresh-cut Lakshmanbhog mango (Mangifera indica L.) slices collected under room temperature conditions for shelf-life analysis. The objective of this dataset is to observe the visual changes occurring in fresh-cut mango slices during storage and to classify the images into Good and Bad quality categories. One ripe Lakshmanbhog mango was selected and peeled manually using a stainless steel knife. The mango pulp was cut into five pieces and each piece was placed separately on a white plastic plate. Images of both the front and back sides of each mango piece were captured using a Realme C25Y smartphone camera (50 MP primary camera). The photographs were taken on a white background under indoor room conditions with artificial lighting. The samples were kept at approximately 30°C room temperature. The mango pieces were placed on a table and remained exposed to ambient conditions during the experiment. Image acquisition started at 10:00 AM on the first day and continued until 1:00 AM. On the second day, image collection resumed from 9:00 AM to 2:00 PM. During the first day, images were captured at 10-minute intervals, while on the second day images were captured at 30-minute intervals. A total of 1100 images were collected: 550 Good images 550 Bad images Visual deterioration of mango slices, including browning, color change, and surface quality degradation, started approximately at 6:00 PM on the first day. Images showing fresh appearance were categorized as Good, while images showing visible deterioration were categorized as Bad. Materials Used: Lakshmanbhog mango Stainless steel knife White plastic plates White background sheet Table surface Realme C25Y smartphone camera This dataset can be used for image processing, machine learning, computer vision, and shelf-life prediction studies of fresh-cut mango slices.本数据集包含为货架期分析采集的、置于室温环境下的鲜切拉克什曼巴格芒果（Lakshmanbhog mango，*Mangifera indica L.*）切片的数码图像。本数据集的研究目标为观测鲜切芒果切片在存储过程中发生的视觉变化，并将图像划分为优质（Good）与劣质（Bad）两个质量类别。 选取一枚成熟的拉克什曼巴格芒果，采用不锈钢刀具手动去皮。将芒果果肉切割为五块，每块单独放置于白色塑料餐盘上。使用Realme C25Y智能手机摄像头（5000万像素主摄）采集每块芒果切片的正面与背面图像。 图像采集于白色背景下的室内环境，采用人工照明。实验过程中，样本置于约30℃的室温环境中，芒果切片放置于桌面并暴露于周围环境条件下。 图像采集于首日上午10:00启动，持续至次日凌晨1:00。次日采集时段为上午9:00至下午2:00。首日图像每10分钟采集一次，次日则调整为每30分钟采集一次。 本次采集共获得1100张图像，其中优质（Good）图像与劣质（Bad）图像各550张。 芒果切片的视觉劣变（包括褐变、色泽变化与表面质量下降）约于首日下午6:00开始出现。外观新鲜的图像被划分为优质类别，出现可见劣变的图像则被划分为劣质类别。 所用实验材料： - 拉克什曼巴格芒果 - 不锈钢刀具 - 白色塑料餐盘 - 白色背景布 - 桌面 - Realme C25Y智能手机摄像头 本数据集可应用于鲜切芒果切片的图像处理、机器学习、计算机视觉以及货架期预测相关研究。