This paper describes the concept, the technical implementation and the practical application of a miniaturized sensor system integrated into an autonomous underwater vehicle (AUV) for real-time acquisition of water quality parameters. The main application field of the presented system is the analysis of the discharge of nitrates into Norwegian fjords near aqua farms. The presented system was developed within the research project SALMON (Sea Water Quality Monitoring and Management) over a three-year period. The development of the sensor system for water quality parameters represented a significant challenge for the research group, as it was to be integrated in the payload unit of the autonomous underwater vehicle in compliance with the underwater environmental conditions. The German company -4H- JENA engineering GmbH (4HJE), with experience in optical in situ-detection of nutrients, designed and built the measurement system. As a carrier platform, the remotely operated vehicle (ROV) “CWolf” from Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung - Institutsteil Angewandte Systemtechnik (IOSB-AST) modified to an AUV was deployed. The concept presented illustrates how the measurement system can be integrated easily into the vehicle with a minimum of hard- and software technical interfaces.

The scope of the project described in this paper is the development of a generalized underwater object detection solution based on Automated Machine Learning (AutoML) principles. Multiple scales, dual priorities, speed, limited data, and class imbalance make object detection a very challenging task. In underwater object detection, further complications come in to play due to acoustic image problems such as non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation, and multipath problems. Therefore, we focus on finding solutions to the problems along the underwater object detection pipeline. A pipeline for realizing a robust generic object detector will be described and demonstrated on a case study of detection of an underwater docking station in sonar images. The system shows an overall detection and classification performance average precision (AP) score of 0.98392 for a test set of 5000 underwater sonar frames.

Water distribution networks are often susceptible to pipeline leaks caused by mechanical damages, natural hazards, corrosion, and other factors. This paper focuses on the detection of leaks in water distribution networks (WDN) using a data-driven approach based on machine learning. A hybrid autoencoder neural network (AE) is developed, which utilizes unsupervised learning to address the issue of unbalanced data (as anomalies are rare events). The AE consists of a 3DCNN encoder, a ConvLSTM decoder, and a ConvLSTM future predictor, making the anomaly detection robust. Additionally, spatial and temporal attention mechanisms are employed to enhance leak localization. The AE first learns the expected behavior and subsequently detects leaks by identifying deviations from this expected behavior. To evaluate the performance of the proposed method, the Water Network Tool for Resilience (WNTR) simulator is utilized to generate water pressure and flow rate data in a water supply network. Various conditions, such as fluctuating water demands, data noise, and the presence of leaks, are considered using the pressure-driven demand (PDD) method. Datasets with and without pipe leaks are obtained, where the AE is trained using the dataset without leaks and tested using the dataset with simulated pipe leaks. The results, based on a benchmark WDN and a confusion matrix analysis, demonstrate that the proposed method successfully identifies leaks in 96% of cases and a false positive rate of 4% compared to two baselines: a multichannel CNN encoder with LSTM decoder (MC-CNN-LSTM) and a random forest and model based on supervised learning with a false positive rate of 8% and 15%, respectively. Furthermore, a real case study demonstrates the applicability of the developed model for leak detection in the operational conditions of water supply networks using inline sensor data.

Tomato cultivation is a vital agricultural practice worldwide, yet it faces significant challenges due to various diseases that adversely affect crop yield and quality. This paper presents a novel tomato disease detection system within an operational framework that leverages an innovative deep learning-based classifier, specifically a Vision Transformer (ViT) integrated with cascaded group attention (CGA) and a modified Focaler-CIoU (Complete Intersection over Union) loss function. The proposed method aims to enhance the accuracy and robustness of disease detection by effectively capturing both local and global contextual information while addressing the challenges of sample imbalance in the dataset. To improve interpretability, we integrate Explainable Artificial Intelligence (XAI) techniques, enabling users to understand the rationale behind the model’s classifications. Additionally, we incorporate a large language model (LLM) to generate comprehensive, context-aware explanations and recommendations based on the identified diseases and other relevant factors, thus bridging the gap between technical analysis and user comprehension. Our evaluation against state-of-the-art deep learning methods, including convolutional neural networks (CNNs) and other transformer-based models, demonstrates that the ViT-CGA model significantly outperforms existing techniques, achieving an overall accuracy of 96.5%, an average precision of 93.9%, an average recall of 96.7%, and an average F1-score of 94.2% for tomato leaf disease classification. The integration of CGA and Focaler-CIoU loss not only contributes to improved model interpretability and stability but also empowers farmers and agricultural stakeholders with actionable insights, fostering informed decision making in disease management. This research advances the field of automated disease detection in crops and provides a practical framework for deploying deep learning solutions in agricultural settings, ultimately supporting sustainable farming practices and enhancing food security.

The prediction of groundwater nitrate concentration's response to geo-environmental and human-influenced factors is essential to better restore groundwater quality and improve land use management practices. In this paper, we regionalize groundwater nitrate concentration using different machine learning methods (Random forest (RF), unimodal 2D and 3D convolutional neural networks (CNN), and multi-stream early and late fusion 2D-CNNs) so that the nitrate situation in unobserved areas can be predicted. CNNs take into account not only the nitrate values of the grid cells of the observation wells but also the values around them. This has the added benefit of allowing them to learn directly about the influence of the surroundings. The predictive performance of the models was tested on a dataset from a pilot region in Germany, and the results show that, in general, all the machine learning models, after a Bayesian optimization hyperparameter search and training, achieve good spatial predictive performance compared to previous studies based on Kriging and numerical models. Based on the mean absolute error (MAE), the random forest model and the 2DCNN late fusion model performed best with an MAE (STD) of 9.55 (0.367) mg/l, R²= 0.43 and 10.32 (0.27) mg/l, R²= 0.27, respectively. The 3DCNN with an MAE (STD) of 11.66 (0.21) mg/l and largest resources consumption is the worst performing model. Feature importance learning from the models was used in conjunction with partial dependency analysis of the most important features to gain greater insight into the major factors explaining the nitrate spatial variability. Large uncertainties in nitrate prediction have been shown in previous studies. Therefore, the models were extended to quantify uncertainty using prediction intervals (PIs) derived from bootstrapping. Knowledge of uncertainty helps the water manager reduce risk and plan more reliably.

In this article, optimal control methods based on a metabolite-constrained fish growth model are applied to the operation of fish production in an aquaponic system. The system is formulated for the twin objective of fish growth and plant fertilization to maximize the benefits by optimal and efficient use of resources from aquaculture. The state equations, basically mass balances, required by the optimization algorithms are given in the form of differential equations for the number of fish in the stock, their average weight as mediated through metabolism and appetite, the water recirculation and waste treatment, hydroponic nutrient requirements and their loss functions. Six parameters, that is, water temperature, flow rate, stock density, feed ration size per fish, energy consumption rate and the quality of food (percentage of digestible proteins) are used to control the system under dynamic conditions. The time to harvest is treated as a static decision variable that is repeatedly adjusted to find the profit-maximizing solution. By modeling the complex interactions between the economic and biological systems, it is possible to obtain the most efficient decisions with respect to diet composition, feeding rates, harvesting time and nutrient releases. Some sample numerical results using data from a tilapia-tomato farm are presented and discussed.