Visual Feature Extraction For Seedling Classification And Analysis In The Plant Seedlings Dataset Using Data Science.
DOI:
https://doi.org/10.24054/raaas.v16i2.4079Keywords:
Data science, agriculture, image processing, feature extraction, histogramsAbstract
This study presents a methodology for the extraction, analysis, and evaluation of visual features from the Plant Seedlings dataset, aimed at enhancing the automatic classification of seedlings at early growth stages. Image processing techniques were applied to extract texture descriptors (LBP and Haralick features), color features (HSV histograms), shape features (contours and geometric metrics), and basic statistical measures (mean, standard deviation, kurtosis, and skewness). A descriptive statistical analysis of the extracted features revealed high variability among variables and low multicollinearity, indicating that the descriptors capture diverse and complementary aspects of the images. The results suggest that combining multiple types of descriptors improves the discriminative power for plant species classification. This methodology lays the foundation for the development of intelligent systems in precision agriculture, integrating electronic engineering and data science to support more efficient and sustainable agronomic decision-making.
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