Published: 20 May 2024| Version 1 | DOI: 10.17632/ywf2329h8m.1
Ranjika Das, Tanmay Sarkar


**Data Description:** The dataset contains over 500 images of Carica papaya, categorized as either "good" or "bad". These images were captured using a Realme 11x Next Era smartphone camera, ensuring consistent image quality and resolution. Each image features a single Carica papaya fruit against a white background, with the data collection process conducted under natural daylight conditions for optimal illumination. **Key Features:** 1. **Image Variation:** The dataset encompasses a diverse range of Carica papaya fruits, capturing variations in size, shape, color, ripeness, and overall condition. This variability is crucial for training robust classification models capable of accurately distinguishing between good and bad fruits. 2. **Annotation:** Each image in the dataset is annotated to indicate whether the Carica papaya fruit is categorized as "good" or "bad". These annotations provide ground truth labels for supervised learning algorithms, facilitating the development of accurate classification models. 3. **Consistent Background:** To ensure uniformity and minimize distractions, all images feature a white background. This consistent background simplifies preprocessing and enables the focus to remain solely on the visual attributes of the Carica papaya fruits. 4. **Daylight Conditions:** The data collection process was conducted under natural daylight conditions to ensure consistent illumination across all images. Natural light enhances the visibility of fruit features and minimizes lighting variations, contributing to the authenticity and quality of the dataset. 5. **High-Quality Images:** Images captured with the Realme 11x Next Era smartphone camera exhibit high resolution and clarity, enabling detailed analysis of fruit characteristics. The quality of the images facilitates precise feature extraction, essential for accurate classification. 6. **Large Dataset Size:** With over 500 images, the dataset provides a significant volume of data for model training and validation. A larger dataset enhances model generalization and reduces the risk of overfitting, leading to improved classification performance on unseen data. **Potential Applications:** 1. **Automated Fruit Classification:** The dataset can be used to develop machine learning models capable of automatically classifying Carica papaya fruits as "good" or "bad" based on their visual characteristics. Such models can assist in quality assessment and sorting processes in agriculture or food industries. 2. **Fruit Quality Control:** By analyzing fruit condition through automated classification, farmers and producers can monitor the quality of Carica papaya fruits and detect potential defects or ripeness issues early. Timely interventions can then be implemented to optimize fruit yield and quality.



Biological Classification, Characterization of Food