Abstract:
Classification of fresh and rotten vegetables is essential in our everyday life as it becomes
difficult sometimes to identify earlier whether the vegetables are fully fresh or not. There
is a possibility of suffering from severe diseases if defect vegetables are consumed. On
the other hand, it will also be difficult to import vegetables in foreign countries if
freshness is not found which may result in deterioration of economy. For this, we have
has been intended to use deep learning method to detect immediately which fresh and
rotten vegetables are fresh and which are rotten. Five vegetables are selected which are-
Cucumber, Green capsicum, pointed gourd, cabbage and tomato. These are divided into
10 distinct classes according to fresh and rotten categories. Fresh and rotten cucumber,
green capsicum, pointed gourd, cabbage and tomato, along with rotten are kept in the tent
box for capturing photos which will be good background for photos as only vegetables
can be recognized easily. At first, 1096 and 1016 raw image data of fresh and rotten
vegetables are captured respectively. This is done by capturing the photos of fresh and
rotten vegetables at various angles. After that these were tilted, rotated at the angles
different from the angles of photos existed during capturing and resized to increase more
in numbers. Among these, 206 images of fresh and 195 images of rotten vegetables are
tested and 830 images of fresh and 786 images of rotten vegetables are trained. Here the
four models of deep learning InceptionV3, MobileNetV2, VGG16 and Xception model
are applied as they can provide good accuracy. InceptionV3 provides the accuracy of
98.50%, accuracy of VGG-16 is 97.01%, MobileNetV2 provides the accuracy of 99.50%
and Xception model has the accuracy of 99.75%. Among these four models, Xception is
the most accurate model.
