Visual inspections of aircraft are a vital aspect of aircraft maintenance. They involve ground personnel walking around the aircraft, mounting scaffolding or ladders, and fully inspecting the aircraft. A visual inspection, however, requires a significant amount of time to perform and may be prone to human error. To address these challenges, a deep-learning-based defect-detection model is proposed, which has the potential to reduce inspection time significantly and enhance inspection reliability. This approach utilises an Unmanned Aircraft System (UAS) equipped with an onboard camera system to capture images for analysis. The proposed machine-learning (ML) model is trained using a deep-learning framework, specifically YOLOv8, on a diverse dataset comprising of both defective and non-defective images of the aircraft. The labelling process for novel classes is accelerated through utilising a low accuracy pre-trained model. Following model analysis, the operator inspects the defects reported in the images. To facilitate efficient identification, object detection techniques, such as bounding boxes, are employed. In this study, various hyperparameter combinations to optimise the model’s performance are explored. Implementing this ML detection system would mitigate the risks associated with human error and improve inspection accuracy, providing a safer visual inspection process for ground personnel and a more efficient solution for aircraft visual inspections for preventative maintenance. Moreover, the utilisation of a UAS enables inspections of inaccessible or hazardous areas without compromising personnel safety. This research demonstrates the potential of deep learning for aircraft visual inspections. The defects analysed are damaged and missing rivets, filiform corrosion, and missing panels, while future research aims to expand the scope to include additional defects such as paint chipping, scratches, burns, and rust. The model’s capability to discern the condition of rivets, panels, and filiform corrosion is evidenced by its achieved validation mean Average Precision (mAP) of 85 %.

The valorisation of food by-products is an important step towards sustainability in food production. Tomatoes constitute one of the most processed crops in the world (160 million tonnes of tomatoes are processed every year), of which 4% is waste. This translates to 6.4 million tonnes of tomato skins and seeds. Currently, this waste is composted or is used in the production of low-value animal feed; higher value can be achieved if this waste stream is re-appropriated for more advanced purposes. Plant cuticle is a membrane structure found on leaves and fruit, including tomatoes, and is mainly composed of cutin. The main function of plant cuticle is to limit water loss from the internal tissue of the plant. Cutin, which can be recovered from the tomato skins by pH shift extraction, has hydrophobic (water repellent) properties and is therefore an ideal raw material for the development of a novel water-resistant coating. In this study, biomass-based bioplastics were developed. Unfortunately, although these bioplastics have good mechanical properties, their hydrophilic nature results in poor water barrier properties. To mitigate this, a very effective water-resistant coating was formulated using the cutin extracted from tomato peels. The water vapour permeability rates of the bioplastics improved by 74% and the percentage swelling of the bioplastic improved by 84% when treated with the cutin coating. With physicochemical properties that can compete with petroleum-based plastics, these bioplastics have the potential to address the growing market demand for sustainable alternatives for food packaging. Using ingredients generated from by-products of the food processing industries (circular economy), the development of these bioplastics also addresses the UN’s Sustainable Development Goal (SDG) 12, Sustainable Consumption and Production (SCP).