Dataset for: Nutrispace: A Novel Color Space to Enhance Deep Learning Based Early Detection of Cucurbits Nutritional Deficiency

This dataset is an extension of our research titled "Nutrispace: A Novel Color Space to Enhance Deep Learning Based Early Detection of Cucurbits Nutritional Deficiency," which is currently under review. For utmost reproducibility, we've included a ZIP file and a Python file: The ZIP file, "dataset_images," comprises 2700 segmented RGB images spanning 9 classes. The dataset is balanced and divided into train, validation, and test sets with a ratio of 0.70:0.15:0.15. Class distribution of the dataset: Crop | Class | Number of samples ---------------|------------------|------------------- Ash gourd | Healthy | 300 Ash gourd | N deficiency | 300 Ash gourd | K deficiency | 300 ---------------|------------------|------------------- Bitter gourd | Healthy | 300 Bitter gourd | N deficiency | 300 Bitter gourd | K deficiency | 300 ---------------|------------------|------------------- Snake gourd | Healthy | 300 Snake gourd | N deficiency | 300 Snake gourd | K deficiency | 300 ---------------|------------------|------------------- | | Total = 2700 The converter.py file is a Python class file for transforming the RGB images to HSV, CIELAB, and our proposed color space - Nutrispace. The .py file contains a short documentation within it for the ease of use.