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Anaerobic digestion (AD) could be considered as a mature technology and nowadays it can still play a pivot role because of the urgent need to provide renewable energy sources and efficiently manage the continuously growing amount of organic waste. Biochar (BC) is an extremely versatile material, which could be produced by carbonization of organic materials, including biomass and wastes, consistently with Circular Economy principles, and "tailor-made" for specific applications. The potential BC role as additive in the control of the many well-known critical issues of AD processes has been increasingly

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Convolutional Neural Networks (CNN) have brought spectacular improvements in several fields of machine vision including object, scene and face recognition. Nonetheless, the impact of this new paradigm on the classification of fine-grained images—such as colour textures—is still controversial. In this work, we evaluate the effectiveness of traditional, hand-crafted descriptors against off-the-shelf CNN-based features for the classification of different types of colour textures under a range of imaging conditions. The study covers 68 image descriptors (35 hand-crafted and 33 CNN-based) and 46 compilations of 23 colour texture datasets divided into 10 experimental conditions. On average, the results indicate a marked superiority of deep networks, particularly with non-stationary textures and in the presence of multiple changes in the acquisition conditions. By contrast, hand-crafted descriptors were better at discriminating stationary textures under steady imaging conditions and proved more robust than CNN-based features to image rotation.

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