Moringa leaves
Description
The dataset I have compiled consists of over 500 images of Moringa leaves, meticulously categorized into "good" and "bad" classes. These images were captured using a Samsung mobile cam J7 Next Era, ensuring consistent image quality and resolution throughout the dataset. To maintain uniformity and minimize distractions, each leaf was photographed against a white background. The data collection process was conducted under daylight conditions, leveraging natural illumination to ensure accurate representation of the leaves' visual characteristics. The key features of this dataset are as follows: Image Variability: The dataset captures a wide range of Moringa leaves, showcasing variations in size, shape, texture, and overall condition. This diversity is essential for training robust classification models capable of accurately distinguishing between healthy and unhealthy leaves. Annotation: Each image in the dataset is meticulously annotated to indicate whether the Moringa leaf falls under the "good" or "bad" category. These annotations serve as ground truth labels for supervised learning algorithms, facilitating the development of accurate classification models. Consistent Background: To maintain consistency and minimize noise, all images feature a uniform white background. This deliberate choice eliminates distractions and ensures that the focus remains solely on the visual attributes of the Moringa leaves. Daylight Conditions: The data collection process was carried out under natural daylight conditions to ensure consistent illumination across all images. Natural light enhances the visibility of leaf features and minimizes lighting variations, contributing to the authenticity and quality of the dataset. High-Quality Images: Images captured with the Samsung mobile cam J7 Next Era exhibit high resolution and clarity, enabling detailed analysis of leaf characteristics. The quality of the images facilitates precise feature extraction, a crucial aspect of building accurate classification models. Large Dataset Size: With over 500 images, the dataset provides a substantial 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.