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Item type:Publication, Low energy and carbon hydroponic tomato cultivation in Northern Spain: Nutritional and environmental assessment(American Chemical Society, 2024-01-15)The agri-food sector is one of the most impactful on the environment in terms of greenhouse gas emissions and is one of the largest consumers of energy and natural resources. In this context, the objective of this study was to evaluate the environmental impacts of hydroponic tomatoes cultivated in northern Spain using a waste product as biomass to heat the greenhouses and solar energy for powering the irrigation system to identify their main environmental hotspots and improvement measures. For this purpose, life cycle assessment (LCA) has been used. The functional units (FUs) chosen were 1 kg of tomatoes and an alternate FU based on nutrition, e.g., NRF9.3. The influence of including biogenic CO2 emissions has been considered using a “-1/+1″ approach to assess the uptake and release of biogenic carbon throughout the whole life cycle. The main results showed that greenhouse gas emissions varied between 1.4 kg CO2 equiv and 2.5 kg CO2 equiv considering biogenic carbon, whereas the total energy demand of the production of hydroponic tomatoes was 9.7 MJ. Fertilization and the greenhouse structure were identified as the main contributors to environmental burdens; hence, improvement opportunities were focused on these critical points. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review(Elsevier B.V., 2024-10-01)The presence of macroplastic (MP) is having serious consequences on natural ecosystems, directly affecting biota and human wellbeing. Given this scenario, estimating MPs' abundance is crucial for assessing the issue and formulating effective waste management strategies. In this context, the main objective of this critical review is to analyze the use of machine learning (ML) techniques, with a particular interest in deep learning (DL) approaches, to detect, classify and quantify MPs in aquatic environments, supported by datasets such as satellite or aerial images and video recordings taken by unmanned aerial vehicles. This article provides a concise overview of artificial intelligence concepts, followed by a bibliometric analysis and a critical review. The search methodology aimed to categorize the scientific contributions through temporal and spatial criteria for bibliometric analysis, whereas the critical review was based on generating homogeneous groups according to the complexity of ML and DL methods, as well as the type of dataset. In light of the review carried out, classical ML techniques, such as random forest or support vector machines, showed robustness in MPs detection. However, it seems that achieving optimal efficiencies in multiclass classification is a limitation for these methods. Consequently, more advanced techniques such as DL approaches are taking the lead for the detection and multiclass classification of MPs. A series of architectures based on convolutional neural networks, and the use of complex pre-trained models through the transfer learning, are currently being explored (e.g., VGG16 and YOLO models), although currently the computational expense is high due to the need for processing large volumes of data. Additionally, there seems to be a trend towards detecting smaller plastic, which need higher resolution images. Finally, it is important to stress that since 2020 there has been a significant increase in scientific research focusing on transformer-based architectures for object detection. Although this can be considered the current state of the art, no studies have been identified that utilize these architectures for MP detection.