Good and Bad classification of Marsh Barbel Leaves (Hygrophila auriculata)

Published: 1 July 2024| Version 1 | DOI: 10.17632/xtrjd3g5ys.1
Suraj Mondal, Tanmay Sarkar


Data Description for Marsh Barbel Leaves Classification Project Overview This dataset contains images of Marsh Barbel Leaves (Hygrophila auriculata) categorized into two classes: good and bad. Each class contains 500 samples, totaling 1000 images. The primary objective of this project is to develop a classification model that can accurately distinguish between good and bad leaves based on visual features. Data Structure Images: High-resolution images of Marsh Barbel Leaves. Labels: Each image is labeled as either "good" or "bad". Features Leaf Color: Variations in shades of green, indicating health or disease. Leaf Texture: Surface texture, which may show signs of damage or disease. Shape and Size: Differences in leaf morphology, which can suggest health status. Spots or Discoloration: Presence of spots or other markings that may indicate disease or poor health. Good Leaves Healthy appearance with uniform green color. Smooth texture and consistent shape. No visible damage or discoloration. Bad Leaves May have visible spots, discoloration, or irregular shapes. Rough texture or signs of wilting. Indicators of disease or poor environmental conditions. Usage The dataset can be used for training and testing machine learning models aimed at classifying leaves. It is suitable for image classification tasks and can be used in various algorithms, such as Convolutional Neural Networks (CNNs). Applications Agricultural Monitoring: Helps in identifying unhealthy plants for early intervention. Environmental Studies: Assists in assessing plant health in different environmental conditions. Educational Tools: Useful for teaching plant pathology and classification techniques.



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