The complete bioconversion of the carbohydrate fraction is of great importance for a lignocellulosic-based biorefinery. However, due to the structure of the lignocellulosic materials, and depending basically on the main parameters within the pretreatment steps, numerous byproducts are generated and they act as inhibitors in the fermentation operations. In this sense, the impact of inhibitory compounds derived from lignocellulosic materials is one of the major challenges for a sustainable biomass-to-biofuel and -bioproduct industry. In order to minimise the negative effects of these compounds, numerous methodologies have been tested including physical, chemical, and biological processes. The main physical and chemical treatments have been studied in this work in relation to the lignocellulosic material and the inhibitor in order to point out the best mechanisms for fermenting purposes. In addition, special attention has been made in the case of lignocellulosic hydrolysates obtained by chemical processes with SO₂, due to the complex matrix of these materials and the increase in these methodologies in future biorefinery markets. Recommendations of different detoxification methods have been given.

Using neural networks on low-power mobile systems can aid in controlling pests while preserving beneficial species for crops. However, low-power devices require simplified neural networks, which may lead to reduced performance. This study was focused on developing an optimized deep-learning model for mobile devices for detecting corn pests. We propose a two-step transfer learning approach to enhance the accuracy of two versions of the MobileNet SSD network. Five beetle species (Coleoptera), including four harmful to corn crops (belonging to genera Anoxia, Diabrotica, Opatrum and Zabrus), and one beneficial (Coccinella sp.), were selected for preliminary testing. We employed two datasets. One for the first transfer learning procedure comprises 2605 images with general dataset classes ‘Beetle’ and ‘Ladybug’. It was used to recalibrate the networks’ trainable parameters for these two broader classes. Furthermore, the models were retrained on a second dataset of 2648 images of the five selected species. Performance was compared with a baseline model in terms of average accuracy per class and mean average precision (mAP). MobileNet-SSD-v2-Lite achieved an mAP of 0.8923, ranking second but close to the highest mAP (0.908) obtained by MobileNet-SSD-v1 and outperforming the baseline mAP by 6.06%. It demonstrated the highest accuracy for Opatrum (0.9514) and Diabrotica (0.8066). Anoxia it reached a third-place accuracy (0.9851), close to the top value of 0.9912. Zabrus achieved the second position (0.9053), while Coccinella was reliably distinguished from all other species, with an accuracy of 0.8939 and zero false positives; moreover, no pest species were mistakenly identified as Coccinella. Analyzing the errors in the MobileNet-SSD-v2-Lite model revealed good overall accuracy despite the reduced size of the training set, with one misclassification, 33 non-identifications, 7 double identifications and 1 false positive across the 266 images from the test set, yielding an overall relative error rate of 0.1579. The preliminary findings validated the two-step transfer learning procedure and placed the MobileNet-SSD-v2-Lite in the first place, showing high potential for using neural networks on real-time pest control while protecting beneficial species.