This comprehensive banana dataset captures important information about banana samples from different regions and varieties. The key attributes are:
ample_id: A unique identifier assigned to each banana sample in the dataset. This allows the samples to be tracked and referenced uniquely.
variety: The cultivar or breed of banana, such as Cavendish, Red Dacca, or Lady Finger. Knowing the specific banana variety provides context about the sample's physical characteristics and growing conditions.
region: The geographic origin of the banana, such as Ecuador, Philippines, or Costa Rica. The region can influence factors like climate, soil, and growing practices that impact the banana's qualities.
quality_score: A numerical score, likely on a scale of 1-4 that rates the overall quality of the banana sample. This could encompass factors like appearance, texture, and lack of defects.
quality_category: A text label that categorizes the quality score into broader groupings like "Excellent" etc
This provides an easier-to-understand quality assessment.
ripeness_index: A numerical index representing the ripeness level of the banana, potentially ranging from 1 (green/unripe) to 10 (overripe). This quantifies the maturity of the fruit.
ripeness_category: A text label like "Green", "Yellow", "Ripe", or "Overripe" that corresponds to the ripeness index. This gives a clear, qualitative ripeness classification.
sugar_content_brix: The sugar content of the banana measured in degrees Brix. This is a common way to assess the sweetness and quality of the fruit.
firmness_kgf: The firmness of the banana measured in kilograms-force. This indicates the texture and maturity of the sample.
length_cm: The physical length of the banana in centimeters. This size metric can vary by variety and growing conditions.