Brassica juncea

Published: 8 April 2024| Version 1 | DOI: 10.17632/n78zj449tv.1
SOUVIK BASAK, Tanmay Sarkar


The dataset comprises over 500 images of pigweed leaves (Brassica juncea album), categorized into two classes: "good" and "bad." These images were captured using a Redmi Note 8 Pro mobile camera against a black background under daylight conditions. **Data Description:** 1. **Classes:** - Good: Represents healthy pigweed leaves that exhibit desired characteristics such as uniform color, absence of discoloration or lesions, and overall vitality. - Bad: Encompasses pigweed leaves showing signs of disease, damage, or other undesirable traits such as discoloration, spots, wilting, or pest infestation. 2. **Image Collection:** - Over 500 images collected in total, with a significant number representing both good and bad instances of pigweed leaves. - Images captured under consistent conditions to maintain uniformity and reduce variability. - Black background utilized to enhance leaf visibility and isolate the subject. 3. **Data Source:** - The images were captured using a Redmi Note 8 Pro mobile camera, ensuring consistent image quality across the dataset. - Daylight conditions were chosen to provide natural lighting, minimizing artificial effects on leaf appearance. 4. **Annotation:** - Each image is labeled according to its class (good or bad), enabling supervised learning tasks. - Annotation may include bounding boxes or masks outlining the leaf area to aid in localization tasks. 5. **Data Preprocessing:** - Images may have undergone preprocessing steps such as resizing, normalization, and background removal to enhance model performance and reduce computational complexity. - Metadata such as image resolution, format, and capture settings may accompany the dataset for reference. 6. **Data Distribution:** - The dataset maintains a balanced distribution between good and bad pigweed leaves, ensuring equal representation of both classes. - Randomization techniques may have been employed during data collection and organization to prevent biases in model training. 7. **Potential Applications:** - The dataset can be utilized for various machine learning tasks, including classification, object detection, and image segmentation. - Applications may include automated agricultural systems for pest detection, disease diagnosis, and crop health monitoring. 8. **Limitations:** - While efforts were made to ensure data consistency and quality, variations in lighting conditions, camera angles, and leaf orientation may introduce some degree of variability. - The dataset primarily focuses on pigweed leaves of Brassica juncea and may not generalize well to other plant species or environmental conditions. In summary, the dataset provides a comprehensive collection of annotated pigweed leaf images suitable for training and evaluating machine learning algorithms in agricultural applications, particularly in the context of plant health assessment and crop management.



Biological Classification, Characterization of Food