2 research outputs found
Detection of sweetness level for fruits (watermelon) with machine learning
The inspection and grading of the watermelon are
done manually but it is a tedious job and it is difficult for the
graders to maintain constant vigilance. Thus, the image
processing has widely been used for identification, detection,
grading and quality evaluation in the agricultural field. The
objective of this work is to investigate the sweetness parameter
for the fruitโs detection and classification algorithm in machine
learnings. This study applies image processing techniques to
detect the color and shape of watermelonโs skin for grading
based on the sweetness level using K-means clustering method
via the Python platform. 13 samples of watermelon images are
used to test the functionality of the proposed detection system in
this study. Then, each watermelon is grouped into Grade A
(high level of sweetness), Grade B (medium level of sweetness),
and Grade C (low level of sweetness) based on its color and
shape detection results. At the end of this research, the proposed
technique resulted in an inaccurate prediction for 2 watermelon
samples out of 13 samples which indicates the system has an
84.62% accuracy in detecting the watermelon sweetness level