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Internet-of-Things (IoT) approaches are continually introducing new sensors into the fields of agriculture and animal welfare. The application of multi-sensor data fusion to these domains remains a complex and open-ended challenge that defies straightforward optimization, often requiring iterative testing and refinement. To respond to this need, we have created a new open-source framework as well as a corresponding Python tool which we call the “Data Fusion Explorer (DFE)”. We demonstrated and evaluated the effectiveness of our proposed framework using four early-stage datasets from diverse disciplines, including animal/environmental tracking, agrarian monitoring, and food quality assessment. This included data across multiple common formats including single, array, and image data, as well as classification or regression and temporal or spatial distributions. We compared various pipeline schemes, such as low-level against mid-level fusion, or the placement of dimensional reduction. Based on their space and time complexities, we then highlighted how these pipelines may be used for different purposes depending on the given problem. As an example, we observed that early feature extraction reduced time and space complexity in agrarian data. Additionally, independent component analysis outperformed principal component analysis slightly in a sweet potato imaging dataset. Lastly, we benchmarked the DFE tool with respect to the Vanilla Python3 packages using our four datasets’ pipelines and observed a significant reduction, usually more than 50%, in coding requirements for users in almost every dataset, suggesting the usefulness of this package for interdisciplinary researchers in the field. 

Guide dogs play a crucial role in enhancing independence and mobility for people with visual impairment, offering invaluable assistance in navigating daily tasks and environments. However, the extensive training required for these dogs is costly, resulting in a limited availability that does not meet the high demand for such skilled working animals. Towards optimizing the training process and to better understand the challenges these guide dogs may be experiencing in the field, we have created a multi-sensor smart collar system. In this study, we developed and compared two supervised machine learning methods to analyze the data acquired from these sensors. We found that the Convolutional Long Short-Term Memory (Conv-LSTM) network worked much more efficiently on subsampled data and Kernel Principal Component Analysis (KPCA) on interpolated data. Each attained approximately 40% accuracy on a 10-state system. Not needing training, KPCA is a much faster method, but not as efficient with larger datasets. Among various sensors on the collar system, we observed that the inertial measurement units account for the vast majority of predictability, and that the addition of environmental acoustic sensing data slightly improved performance in most datasets. We also created a lexicon of data patterns using an unsupervised autoencoder. We present several regions of relatively higher density in the latent variable space that correspond to more common patterns and our attempt to visualize these patterns. In this preliminary effort, we found that several test states could be combined into larger superstates to simplify the testing procedures. Additionally, environmental sensor data did not carry much weight, as air conditioning units maintained the testing room at standard conditions. 

The study of plant electrophysiology offers promising techniques to track plant health and stress in vivo for both agricultural and environmental monitoring applications. Use of superficial electrodes on the plant body to record surface potentials may provide new phenotyping insights. Bacterial nanocellulose (BNC) is a flexible, optically translucent, and water-vapor-permeable material with low manufacturing costs, making it an ideal substrate for non-invasive and non-destructive plant electrodes. This work presents BNC electrodes with screen-printed carbon (graphite) ink-based conductive traces and pads. It investigates the potential of these electrodes for plant surface electrophysiology measurements in comparison to commercially available standard wet gel and needle electrodes. The electrochemically active surface area and impedance of the BNC electrodes varied based on the annealing temperature and time over the ranges of 50 °C to 90 °C and 5 to 60 min, respectively. The water vapor transfer rate and optical transmittance of the BNC substrate were measured to estimate the level of occlusion caused by these surface electrodes on the plant tissue. The total reduction in chlorophyll content under the electrodes was measured after the electrodes were placed on maize leaves for up to 300 h, showing that the BNC caused only a 16% reduction. Maize leaf transpiration was reduced by only 20% under the BNC electrodes after 72 h compared to a 60% reduction under wet gel electrodes in 48 h. On three different model plants, BNC–carbon ink surface electrodes and standard invasive needle electrodes were shown to have a comparable signal quality, with a correlation coefficient of >0.9, when measuring surface biopotentials induced by acute environmental stressors. These are strong indications of the superior performance of the BNC substrate with screen-printed graphite ink as an electrode material for plant surface biopotential recordings.