Using similarity learning to enable decision support in aquaculture

Abstract

Aquaculture (AQ) is an industry that cultivates food in water. This includes many types of seafood such as salmon, trout, and whitefish, as well as shellfish and algae. Farms for seafood production are typically described as sites by the industry. In Norway, the site locations are normally regulated and allocated by the government. Artificial intelligence (AI) and machine learning (ML) has not yet been widely adopted in the industry. AI/ML would potentially be able to support the industry in automation, operation and decision support. The aquaculture industry is expanding across the globe. This is a result of technological development and the need for more food production to feed a growing population. In 2012, the Norwegian seafood industry was expected to grow five-fold from 2007 until 2050 [1]. According to industry representatives and the government1,2, this is still the case today. As a result of this expansion, the industry needs to increase the number of production sites. While expanding, the industry needs to keep the environmental impact of such production sites to a minimum. As production sites pollute their immediate surroundings, they should ideally not be in constant production over a long period of time. Additionally, the production sites cannot be too concentrated geographically to minimize the environmental impact and risk of spreading diseases such as sea lice. As a result, the number of available sites is decreasing, and the industry now looks to increasingly more exposed locations for their aquaculture operations. Exposed aquaculture sites are subject to rough conditions and are often inaccessible. Typical aquaculture sites are well sheltered. To ensure the same level of safety, aquaculture sites that are more exposed would require more resources and a more robust physical infrastructure. Also, the level of exposure often leads to more downtime, where personnel is waiting for the weather to clear up to perform their tasks. The aquaculture industry is a conservative industry and has not progressed far in terms of digitalization and instrumentation compared to many other comparable industries such as oil and gas. The push towards more exposed aquaculture operations is now changing this, where increasing the level of automation and remote work would significantly contribute to decreasing the risks to personnel. Such automated operations require the application of digital technologies both for operations and decision-support. This development is supported by the availability of more operational data from the aquaculture industry in recent years. As a result, the connectivity and data availability allows for data-driven services and utilization of ML. Data-driven models and ML support in the aquaculture industry include both operational use cases and decision support systems (DSSs). Operational use cases for aquaculture include 1) computer vision for situation recognition needed for automatic fish feeding, and 2) robotics that can perform necessary operations such as cage cleaning or extracting fish. As such, operational use cases are use cases where ML models are used in real-time or close to real-time. In contrast, DSSs are typically used as a planning tool. DSSs use data-driven models in the context of supporting decision-making or operational planning. Such systems are designed to help operators by predicting operational properties, such as production, structure movements, or waves. Most decision-makers, especially from conservative industries, prefer an understandable and explainable DSS. When the DSS explains the recommendation it produces, it increases the trust in that recommendation, and as a result, the usefulness of the DSS. Many machine learning methods and their resulting models are not easy to explain to most users. One way of alleviating this is to use case-based reasoning (CBR)[2]. CBR captures previous experiences or situations in the form of cases that consist of a problem description and the corresponding solution. As part of a DSS, CBR would store previous situations where the DSS was used and the resulting action or solution. In this way, the DSS user can be presented with the previous situation most similar to the current situation and the resulting action for that situation. The input of a DSS can be the current state. In the case of using CBR for planning in a DSS, the CBR input can be a prediction (e.g., a predicted situation for which the CBR can retrieve a solution). Presenting an actual recorded situational experience and resulting action along with the prediction provides an indirect explanation and strengthens the user’s confidence in the DSS. The work described in this thesis investigates the use of machine learning to increase the level of automation in aquaculture operations, focusing on decision support. A general framework for designing a DSS is introduced, from data gathering to the user interface. This framework outlines the steps from sensors readings, preprocessing of the data, combining the data with knowledge and experience from the users of the DSS, using the data to feed machine learning, knowledge models, and numerical models to then predict a future state which can be used to make informed decisions. In addition, a CBR-based DSS can store previously recorded situations where the DSS was applied (cases). The DSS can then use this repository to retrieve and present the user with the previously recorded cases that are most similar to the predicted state. To do this, the DSS must retrieve the case most relevant (similar) to the one predicted by the DSS or input by the DSS user (query case). Retrieving the most similar case requires the DSS to compute the similarity between the query case and the cases in the repository. Measuring similarity between cases is a focus of research within machine learning and case-based reasoning. Manual modeling this similarity can be challenging. Building on previous state-of-the-art machine learning methods, we propose a new method for learning such similarity measures from data (similarity learning), which can be used for retrieving cases: Extended Siamese Neural Networks (ESNN). ESNN is a similarity learning (SL) method that outperforms the accuracy and training speed of state-of-the-art methods across domains. Extending the testing of ESNN, we developed a dataset for describing situations in aquaculture operations. We demonstrated that ESNN also outperformed state-of-the-art methods for retrieving the most similar operational situations.