  India is the second largest producer of fruits and vegetables in the world,
and one of the largest consumers of fruits like Banana, Papaya and Mangoes
through retail and ecommerce giants like BigBasket, Grofers and Amazon Fresh.
However, adoption of technology in supply chain and retail stores is still low
and there is a great potential to adopt computer-vision based technology for
identification and classification of fruits. We have chosen banana fruit to
build a computer vision based model to carry out the following three use-cases
(a) Identify Banana from a given image (b) Determine sub-family or variety of
Banana (c) Determine the quality of Banana. Successful execution of these
use-cases using computer-vision model would greatly help with overall inventory
management automation, quality control, quick and efficient weighing and
billing which all are manual labor intensive currently. In this work, we
suggest a machine learning pipeline that combines the ideas of CNNs, transfer
learning, and data augmentation towards improving Banana fruit sub family and
quality image classification. We have built a basic CNN and then went on to
tune a MobileNet Banana classification model using a combination of
self-curated and publicly-available dataset of 3064 images. The results show an
overall 93.4% and 100% accuracy for sub-family/variety and for quality test
classifications respectively.
