Image processing for corn quality classification using convolutional neural network (CNN) method.

Corn consumption in Indonesia can reach around 14.37 million tons and continues to increase every year. This increase is directly proportional to the demand from corn processing industries/factories/warehouses to farmers for the supply of corn ingredients. However, the industry often finds corn with quality that is not suitable for production from farmers/collectors, this causing losses for industries. This problem is very important to overcome so that losses can be reduced and the results of corn processing production can also have good quality. This research proposes an image processing solution for industrial corn quality classification using the Convolutional Neural Network (CNN) method. This research trains the CNN to be able to recognize the quality of corn by utilizing 600 images/images of corn as a dataset consisting of 300 images of good quality corn and 300 images of poor quality corn. The CNN method is then used as an algorithm to recognize good quality corn and which is not. The research results so far show an accuracy rate of 99.1% with 10 epoch training. [ABSTRACT FROM AUTHOR]