Normal and Peaberry Coffee Beans Classification from Green Coffee Bean Images Using Convolutional Neural Networks and Support Vector Machine

The aim of this study is to develop a system which can
identify and sort peaberries automatically at low cost for coffee
producers in developing countries. In this paper, the focus is on the
classification of peaberries and normal coffee beans using image
processing and machine learning techniques. The peaberry is not bad
and not a normal bean. The peaberry is born in an only single seed,
relatively round seed from a coffee cherry instead of the usual flatsided
pair of beans. It has another value and flavor. To make the taste
of the coffee better, it is necessary to separate the peaberry and
normal bean before green coffee beans roasting. Otherwise, the taste
of total beans will be mixed, and it will be bad. In roaster procedure
time, all the beans shape, size, and weight must be unique; otherwise,
the larger bean will take more time for roasting inside. The peaberry
has a different size and different shape even though they have the
same weight as normal beans. The peaberry roasts slower than other
normal beans. Therefore, neither technique provides a good option to
select the peaberries. Defect beans, e.g., sour, broken, black, and fade
bean, are easy to check and pick up manually by hand. On the other
hand, the peaberry pick up is very difficult even for trained specialists
because the shape and color of the peaberry are similar to normal
beans. In this study, we use image processing and machine learning
techniques to discriminate the normal and peaberry bean as a part of
the sorting system. As the first step, we applied Deep Convolutional
Neural Networks (CNN) and Support Vector Machine (SVM) as
machine learning techniques to discriminate the peaberry and normal
bean. As a result, better performance was obtained with CNN than
with SVM for the discrimination of the peaberry. The trained
artificial neural network with high performance CPU and GPU in this
work will be simply installed into the inexpensive and low in
calculation Raspberry Pi system. We assume that this system will be
used in under developed countries. The study evaluates and compares
the feasibility of the methods in terms of accuracy of classification
and processing speed.