CLASSIFICATION OF INJERA QUALITY USING CONVOLUTIONAL NEURAL NETWORK

Abstract

INJERA is one of the most well-known Ethiopian foods. It is also a popular food all over the world because of its whole grain product and gluten-free nature. It is prepared from different grains like teff, maize, barley, etc. but teff is the most preferred grain and it contains many nutrients to have the advantage to our health. It can cause celiac disease and controls blood sugar levels. Nowadays, many enterprises are occupied with selling INJERA to hotels and individuals and also exporting to foreign countries. But, some of the enterprises sell INJERA by adulterating with foreign particles without the knowledge of consumers to gain their lone profit. Due to this, INJERA adulteration has become a serious problem now & this might be a health risk in the near future. This Adulterated INJERA is dangerous because it can be toxic and may affect one’s health. It could deprive nutrients crucial for proper growth and development. Classification of quality INJERA using the naked eyes and also through smelling, observing the appearance, and tasting is difficult due to their visual similarities. To address this problem, we proposed a deep learning algorithm for the classification of INJERA quality based on the INJERA images. To do so, the design science research methodology was followed. To conduct this study, a total of 2230 images were collected including 1115 pure Teff INJERA samples and 1115 mixed INJERA samples. After collecting the required images, we applied image pre processing such as image resizing, and image normalization on the image datasets before adding them to the model. In this study three different CNN models with different design options like the number of layers, stride size, kernel size, and padding and with and without dropout namely the CNN3L, CNN4L, and CNN5L, were trained to determine the quality of INJERA as pure or adulterated. All of the models were able to find a successful detection result after many experiments. The CNN3L model has 99.94 percent training and 99.11 percent testing accuracy, the CNN4L pre-trained model has 99.78 percent training and 99.55 percent testing accuracy, and the CNN5L has 99.89 percent training and 99.55 percent testing accuracy. The CNN3L model has a training loss of 0.72% and a testing loss of 2.80%, the CNN4L model has a training loss of 3.74% and a testing loss of 10.1%, and the CNN5L model has a training loss of 0.86 percent and a testing loss of 1.94 percent. The experimental result demonstrates that the CNN5L model is effective for the accurate recognition of INJERA images with higher accuracy (99.55 %) and less loss value (1.94 %) in this study