Abstract
Crop byproducts are alternatives to nonrenewable energy resources. Burning biomass results in lower emission of undesirable nitrogen and sulfur oxides and contributes no significant greenhouse effect. There is a diverse range of energy-useful biomass, including in terms of calorific value. This article presents a new method of discriminating biomass, and of determining its calorific value. The method involves extracting the selected texture features on the surface of a briquette from a microscopic image and then classifying them using supervised classification methods. The fractal dimension, local binary pattern (LBP), and Haralick features are computed and then classified by linear discrimination analysis (LDA). The discrimination results are compared with the results obtained by random forest (RF) and deep neural network (DNN) type classifiers. This approach is superior in terms of complexity and operating time to other methods such as, for instance, the calorimetric method or analysis of the chemical composition of elements in a sample. In the normal operation mode, our method identifies the calorific value in the time of about 100 s, i.e., 90 times faster than traditional combustion of material samples. In predicting from a single sample image, the overall average accuracy of 95% was achieved for all tested classifiers. The authors’ idea to use ten input images of the same material and then majority voting after classification increases the discrimination system accuracy above 99%.