Elizabeth Sklar

Madeleine Darbyshire, Elizabeth Sklar, Simon Parsons

Advancements in machine vision that enable detailed inferences to be made from images have the potential to transform many sectors including agriculture. Precision agriculture, where data analysis enables interventions to be precisely targeted, has many possible applications. Precision spraying, for example, can limit the application of herbicide only to weeds, or limit the application of fertiliser only to undernourished crops, instead of spraying the entire field. The approach promises to maximise yields, whilst minimising resource use and harms to the surrounding environment. To this end, we propose a hierarchical panoptic segmentation method to simultaneously identify indicators of plant growth and locate weeds within an image. We adapt Mask2Former, a state-of-the-art architecture for panoptic segmentation, to predict crop, weed and leaf masks. We achieve a PQ† of 75.99. Additionally, we explore approaches to make the architecture more compact and therefore more suitable for time and compute constrained applications. With our more compact architecture, inference is up to 60% faster and the reduction in PQ† is less than 1%.

Madeleine Darbyshire, Elizabeth Sklar, Simon Parsons

Precision agriculture leverages data and machine learning so that farmers can monitor their crops and target interventions precisely. This enables the precision application of herbicide only to weeds, or the precision application of fertilizer only to undernourished crops, rather than to the entire field. The approach promises to maximize yields while minimizing resource use and harm to the surrounding environment. To this end, we propose a hierarchical panoptic segmentation method that simultaneously determines leaf count (as an identifier of plant growth)and locates weeds within an image. In particular, our approach aims to improve the segmentation of smaller instances like the leaves and weeds by incorporating focal loss and boundary loss. Not only does this result in competitive performance, achieving a PQ+ of 81.89 on the standard training set, but we also demonstrate we can improve leaf-counting accuracy with our method. The code is available at https://github.com/madeleinedarbyshire/HierarchicalMask2Former.

Federico Castagna, Nadin Kokciyan, Isabel Sassoon et al.

Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.

Madeleine Darbyshire, Shaun Coutts, Eleanor Hammond et al.

As the burden of herbicide resistance grows and the environmental costs of excessive herbicide use become clear, new approaches to managing weed populations are needed. This is particularly true for cereal crops, like wheat and barley, that are staple foods and occupy a globally significant share of farmland. Even modest advances in weed management practices across these crops could deliver major benefits for both the environment and food security. Blackgrass is a major grass weed which causes particular problems in cereal crops in north-west Europe, a major cereal production area, because it has high levels of herbicide resistance. Detecting blackgrass is also difficult due to its similarity to cereals. Yet, a systematic review of the literature on weed recognition in wheat and barley, included in this study, highlights that blackgrass - and grass weeds more broadly - have received less research attention compared to certain broadleaf weeds. With the use of machine vision and multispectral imaging, we investigate the effectiveness of state-of-the-art methods to identify blackgrass in wheat and barley crops. As part of this work, we present the Eastern England Blackgrass Dataset, a large dataset with which we evaluate several key aspects of blackgrass weed recognition. Firstly, we determine the performance of different CNN and transformer-based architectures on images from unseen fields. Secondly, we demonstrate the role that different spectral bands have on the performance of weed classification. Lastly, we evaluate the role of dataset size in classification performance for each of the models trialled. All models tested achieved an accuracy greater than 80%. Our best model achieved 89.6% and that only half the training data was required to achieve this performance. Our dataset is available at: https://lcas.lincoln.ac.uk/wp/research/data-sets-software/eastern-england-blackgrass-dataset .

Adrian Salazar-Gomez, Madeleine Darbyshire, Junfeng Gao et al.

The evolution of smaller, faster processors and cheaper digital storage mechanisms across the last 4-5 decades has vastly increased the opportunity to integrate intelligent technologies in a wide range of practical environments to address a broad spectrum of tasks. One exciting application domain for such technologies is precision agriculture, where the ability to integrate on-board machine vision with data-driven actuation means that farmers can make decisions about crop care and harvesting at the level of the individual plant rather than the whole field. This makes sense both economically and environmentally. However, the key driver for this capability is fast and robust machine vision -- typically driven by machine learning (ML) solutions and dependent on accurate modelling. One critical challenge is that the bulk of ML-based vision research considers only metrics that evaluate the accuracy of object detection and do not assess practical factors. This paper introduces three metrics that highlight different aspects relevant for real-world deployment of precision weeding and demonstrates their utility through experimental results.

Eric Schneider, Marcus Poulton, Archie Drake et al.

Multi-Robot Task Allocation (MRTA) is the problem of distributing a set of tasks to a team of robots with the objective of optimising some criteria, such as minimising the amount of time or energy spent to complete all the tasks or maximising the efficiency of the team's joint activity. The exploration of MRTA methods is typically restricted to laboratory and field experimentation. There are few existing real-world models in which teams of autonomous mobile robots are deployed "in the wild", e.g., in industrial settings. In the work presented here, a market-based MRTA approach is applied to the problem of ambulance dispatch, where ambulances are allocated in respond to patients' calls for help. Ambulances and robots are limited (and perhaps scarce), specialised mobile resources; incidents and tasks represent time-sensitive, specific, potentially unlimited, precisely-located demands for the services which the resources provide. Historical data from the London Ambulance Service describing a set of more than 1 million (anonymised) incidents are used as the basis for evaluating the predicted performance of the market-based approach versus the current, largely manual, method of allocating ambulances to incidents. Experimental results show statistically significant improvement in response times when using the market-based approach.

Martin Chapman, Vasa Curcin, Elizabeth I Sklar

HL7's Fast Healthcare Interoperability Resources (FHIR) standard is designed to provide a consistent way in which to represent and exchange healthcare data, such as electronic health records (EHRs). SMART--on--FHIR (SoF) technology uses this standard to augment existing healthcare data systems with a standard FHIR interface. While this is an important goal, little attention has been paid to developing mechanisms that convert EHR data structured using proprietary schema to the FHIR standard, in order to be served by such an interface. In this paper, a formal process is proposed that both identifies a set of FHIR resources that best capture the elements of an EHR, and transitions the contents of that EHR to FHIR, with a view to supporting the operation of SoF containers, and the wider interoperability of health records with the FHIR standard. This process relies on a number of techniques that enable us to understand when two terms are equivalent, in particular a set of similarity metrics, which are combined along with a series of parameters in order to enable the approach to be tuned to the different EHR standards encountered. Thus, when realised in software, the translation process is semi-autonomous, requiring only the specification of these parameters before performing an arbitrary number of future conversions. The approach is demonstrated by utilising it as part of the CONSULT project, a wider decision support system that aims to provide intelligent decision support for stroke patients.

Shaodian Zhang, Edouard Grave, Elizabeth Sklar et al.

Identifying topics of discussions in online health communities (OHC) is critical to various applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics. We apply the CNN classifier to the most popular breast cancer online community, and carry out a longitudinal analysis to show topic distributions and topic changes throughout members' participation. Our experimental results suggest that CNN outperforms other classifiers in the task of topic classification, and that certain trajectories can be detected with respect to topic changes.