Good and Bad classification of Brassica Oleracea flower (Cauliflower)
Description
For the project titled "Good and Bad Classification of Brassica oleracea Flower (Cauliflower)," a comprehensive dataset has been compiled containing 1,000 samples of Marsh Barbel Leaves, equally divided into 500 good samples and 500 bad samples. This dataset serves as the foundation for developing a classification model aimed at distinguishing between good and bad samples of cauliflower flowers based on their associated Marsh Barbel Leaves. ### Data Description The dataset comprises the following key features: 1. *Image Data*: - Each sample includes high-resolution images of Marsh Barbel Leaves. - Images are standardized in terms of resolution and format to ensure consistency. - Various angles and lighting conditions are considered to mimic real-world scenarios. 2. *Leaf Quality Label*: - Each image is annotated with a binary label indicating its quality: - 0 for bad samples. - 1 for good samples. - Labels are determined by expert agronomists to ensure accuracy. 3. *Visual Attributes*: - *Color*: Variations in leaf color, ranging from healthy green to diseased yellow or brown, are noted. - *Texture*: Differences in leaf surface texture, including signs of wilting or crispness. - *Shape*: Anomalies in leaf shape, such as irregular edges or deformities. - *Spots and Lesions*: Presence of spots, lesions, or other visible signs of disease or pest infestation. 4. *Metadata*: - *Capture Date and Time*: Timestamp for when each image was captured. - *Location*: Geographical data indicating where the leaf samples were collected. - *Weather Conditions*: Environmental factors at the time of capture, which might influence leaf quality. - *Growth Stage*: The developmental stage of the cauliflower plant, which can impact leaf appearance. ### Data Collection and Preprocessing 1. *Collection Method*: - Leaves were collected from various cauliflower fields to ensure diversity in the dataset. - Images were taken using high-resolution cameras under controlled conditions. 2. *Preprocessing Steps*: - *Image Normalization*: All images are resized to a uniform dimension to facilitate model training. - *Data Augmentation*: Techniques such as rotation, flipping, and scaling are applied to increase the dataset's variability and robustness. - *Label Encoding*: Quality labels are encoded as binary values to simplify the classification task. ### Dataset Utilization This dataset is intended to train and evaluate machine learning models for the classification of Marsh Barbel Leaves quality, which indirectly assesses the health of Brassica oleracea flowers (cauliflower). The ultimate goal is to develop a reliable system that can aid farmers and agronomists in early detection of diseases or deficiencies in cauliflower crops, leading to timely interventions and improved crop yield. ### Potential Applications - *Automated Crop Monitoring*: Integration of the model into automated systems for continuous mon