Flesh of pumpkin from ecological farming as part of fruit suitable for non-destructive cultivar classification using computer vision

Abstract

The aim of this study was to build the discriminative models for distinguishing the different cultivars of flesh of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ based on selected textures of the outer surface of images of cubes. The novelty of research involved the use of about 2000 different textures for one image. The highest total accuracy (98%) of discrimination of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ was determined for models built based on textures selected from the color space Lab and the IBk classifier and some of the individual cultivars were classified with the correctness of 100%. The total accuracy of up to 96% was observed for color space RGB and 97.5% for color space XYZ. In the case of color channels, the total accuracies reached 91% for channel b, 89.5% for channel X, 89% for channel Z.

Machine Learning-Based Classification of Mulberry Leaf Diseases

“…Plant leaf diseases have garnered significant attention due to their considerable effects on agriculture and daily life [10,11]. The conventional methods of disease identification are timeconsuming and prone to errors, leading to the development of innovative techniques based on deep learning.…”

VPBR: An Automatic and Low-Cost Vision-Based Biophysical Properties Recognition Pipeline for Pumpkin

“…Dang et al showcased the potential of UAV-based RGB imagery in reducing labor and time for radish disease monitoring, making it a cost-effective and efficient tool for precision agriculture [ 11 ]. Ropelewska et al presented a non-destructive method for classifying different pumpkin cultivars based on flesh characteristics [ 12 ], employing various CV methods. The experimental results showed an accurate classification of pumpkin cultivars based on flesh characteristics with over 90% accuracy.…”