BiomassID: A biomass type identification system for mobile devices

Biomass quality assessment is of great importance when one in the biomass industry needs to produce another energy product, such as biofuel or bioenergy, for instance. Usually, the biomass quality is determined using expensive devices, such as mass spectrometers, or complex chemical tests that may need several days to complete. Because of the high costs of such methods, people tend to forsake biomass quality assessment and move on to directly produce bioproducts from any kind of biomass. In this paper, a cheap and fast solution for biomass type identification is proposed and investigated. The quality of biomass can be inferred at a coarse level from the type of biomass. In the proposed approach, biomass type identification is treated as a texture classification problem. A texture classification system developed for mobile devices which is able to distinguish between four types of biomass texture images is presented in this paper. Several state of the art texture classification systems based on machine learning are evaluated in a series of experiments on a data set of biomass texture images. The experiments are conducted to determine the system that can offer the best trade-off between accuracy and speed, since the goal is to implement it on a mobile device with limited processing power and memory. In the end, the selected system can identify the type of biomass from pictures taken with a mobile device camera in a few seconds directly on the respective mobile device. The utility of the system is demonstrated through an iOS application that is freely available for download in the App Store at http://appstore.com/biomassid.