Identification of co-substrate composted with sewage sludge using convolutional neural networks

In this paper an attempt was made to build classification models, based on convolutional neural networks, for identification of co-substrate composted with sewage sludge. Due to the pilot character of the studies, they were limited to two co-substrates, i.e. maize straw and rapeseed straw. In total, 12 composting experiments were carried out, each half of them with the content of each of the adopted types of straw. As a result of experiments, 2304 images of composted material samples were obtained, and they bacame the input information for the neural networks. Classification models were developed using the Tensorflow environment, TFLearn library and Python programming language. In their structure, one convolutional layer with different number of convolutional filters and one pooling layer were used to extract image features, and also two fully-connected layers were adopted for classification purposes. The training of the network was carried out with the use of the Adam optimization algorithm. Finally, 4 convolutional neural networks were developed, and their classification error estimated for the test set ranged from 4.1 to 11.0%.