The ubiquity of sensors and IoT devices has led to an explosion in data availability in modern agriculture. The large volume and heterogeneity of the data, together with the complexity of data processing requirements, pose huge obstacles for achieving the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). Current data management and analysis paradigms are to a large extent traditional, in which data collecting, curating, integration, loading, storing, sharing and analyzing still involve too much human effort and know-how. The experts, researchers and the farm operators need to understand the data and the whole process of data management pipeline to make full use of the data. The essential problem of the traditional paradigm is the lack of a layer of orchestrational intelligence which can understand, organize and coordinate the data processing utilities to maximize data management and analysis outcome. The emerging reasoning and tool mastering abilities of large language models (LLM) make it a potentially good fit to this position, which helps a shift from the traditional user-driven paradigm to AI-driven paradigm. In this paper, we propose and explore the idea of a LLM based copilot for autonomous agricultural data management and analysis. Based on our previously developed platform of Agricultural Data Management and Analytics (ADMA), we build a proof-of-concept multi-agent system called ADMA Copilot, which can understand user’s intent, makes plans for data processing pipeline and accomplishes tasks automatically, in which three agents: a LLM based controller, an input formatter and an output formatter collaborate together. Different from existing LLM based solutions, by defining a meta-program graph, our work decouples control flow and data flow to enhance the predictability of the behavior of the agents. Experiments demonstrate the intelligence, autonomy, efficacy, efficiency, extensibility, flexibility and privacy of our system. Comparison is also made between ours and existing systems to show the superiority and potential of our system.

Approximate Nearest Neighbor (ANN) search has become a fundamental operation in numerous applications, including recommendation systems, computer vision, and natural language processing. The advent of Large Language Models (LLMs) arouses new interest in developing more efficient ANN algorithms, which will be the core functionality of vector databases as long-term memory of LLM. Multiple types of index structures, such as hashing-based, tree-based, and quantization-based, have been developed for ANN, and recently, graph-based algorithms have become the SOTA paradigm with the best trade-off between recall rate and query latency. However, almost all the existing graph-based index structures can only be hosted in memory due to the otherwise frequent I/O operations during searching if the graph-based index is stored on disk. The problem follows that for extremely large datasets, it is infeasible to accommodate the whole graph-based index in memory, and furthermore, it is difficult to build the whole index in memory at once. Thus, it is favorable if the graph-based index can be stored purely on disk and loaded into memory on demand during searching on the graph. There are existing efforts, such as DiskANN, which try to store graph-based index structure on disk while still keeping a compressed version of the dataset in memory to reduce disk I/O and speed up distance calculation. In this paper, we introduce LM-DiskANN, a novel dynamic graph-based ANN index that is designed specifically to be hosted on disk while keeping a low memory footprint by storing complete routing information in each node. By conducting extensive experiments on multiple benchmark datasets, we demonstrate that LM-DiskANN achieves a similar recall-latency curve while consuming much less memory compared with SOTA graph-based ANN indexes. Furthermore, its scalability and adaptability make it a promising solution for future big data applications.

Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber with population growth under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems. As various sensors and Internet of Things (IoT) instrumentation become more available, affordable, reliable, and stable, it has become possible to conduct data collection, integration, and analysis at multiple temporal and spatial scales, in real-time, and with high resolutions. At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the de facto data management and analysis practices adopted by scientists have become increasingly inefficient. Additionally, the data generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets across disciplines often do not share the same ontology, modality, or format. All of the above make it necessary to design a new data management infrastructure that implements the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data Management and Analytics (ADMA), which satisfies the FAIR principles. Our new data management infrastructure is intelligent by supporting semantic data management across disciplines, interactive by providing various data management/analysis portals such as web GUI, command line, and API, scalable by utilizing the power of high-performance computing (HPC), extensible by allowing users to load their own data analysis tools, trackable by keeping track of different operations on each file, and open by using a rich set of mature open source technologies.

Medical image synthesis is important in diverse healthcare applications, such as computer-aided diagnosis, medical image analysis, and educational tools. While Generative Adversarial Networks (GANs) have shown remarkable success in generating natural images, their application to medical images often falls short in faithfully capturing essential anatomical features. In this paper, we introduce a new approach that focuses on tissue-specific color encoding to enhance medical image synthesis using GANs. Our method deviates from the conventional practice of directly training GANs on gray-scale medical images. Instead, we initiate the process by generating and encoding various gray-scale representations of distinct tissues into separate color channels within composite images. These tissue-specific color images are then utilized to train a GAN model. The GAN, once trained, excels in producing high-quality synthetic images for individual tissues, and when combined, these tissue images yield final synthesized images that better portray the intricate tissue characteristics found in medical data. We have conducted an experimental study to validate the effectiveness of our approach in comparison to alternative methods with both qualitative and quantitative assessments to evaluate the quality of synthesized individual tissues and their combined final results.

Cover crops offer a range of agricultural and environmental benefits, such as reducing soil erosion, increasing carbon and enhancing water storage, increasing forage production, protecting soil nutrients, and so on. However, adoption of cover crop farming remains limited among Nebraska’s farmers. To promote awareness of the value of cover crop farming, we harness modern tools and technology and develop a new web-based tool capable of quantifying potential forage production, forage quality, and environmental benefits when planting cover crops, taking into account factors like climate, soil types, and seeding periods. Our tool incorporates the concept of data provenance to capture simulation configurations and results. This implementation can enhance data integrity and facilitate knowledge sharing within the scientific community, supporting further research and broader public benefits. The tool also includes reporting functions with visualizations illustrating distributions of potential forage, transpiration, nitrogen uptake, and more. Based on factors such as cover crop types, planting and termination dates, locations, and soil types, our tool provides valuable insights, enabling farmers to experiment with different cover crops on their land, ultimately leading to improved environmental outcomes for the broader Nebraska community.

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. It computes values and derivatives with high-order accuracy throughout the spatial domain, mitigating artifacts associated with zero- or first-order interpolation. However, the slow query time through MFA makes it less suitable for interactively visualizing a large MFA model. In this work, we develop the first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA model encoded from large-scale datasets. Our method achieves low input latency through distributed architecture, and its performance can be further enhanced by utilizing a compressed MFA model while still maintaining a high-quality rendering result for scientific datasets. We conduct comprehensive experiments to show that MFA-DVV can decrease the input latency and achieve superior visualization results for big scientific data compared with existing approaches.

Soil measurement and evaluation are crucial to various aspects of agriculture, including agricultural productivity, nutrient management, water management, and pH Regulation. Hyperspectral imaging is an advanced technique used to capture and analyze a wide range of light wavelengths (or spectral bands) across the electromagnetic spectrum. Hyperspectral imaging in soil research involves the use of this advanced imaging technique to analyze the spectral properties of soils. It allows researchers to capture detailed information about the composition, texture, and conditions of soil across a wide range of wavelengths in the electromagnetic spectrum. This in-depth spectral analysis provides valuable insights for studying soil health, nutrient content, moisture levels, and other critical parameters. However, existing hyperspectral analysis of soil relies on using imaging systems to exclusively capture information from the soil surface. This yields a two-dimensional image in which each pixel represents a spectrum vector. In this paper, we provide a new 3D hyperspectral data capturing features deep into the soil where each voxel represents a spectrum vector. For effective analysis of this type of new hyperspectral data, we develop a 3D visualization tool to not only directly visualize individual spectrum of the soil volume but also provide a way to cluster such high dimensional data leveraging a deep learning-based method through autoencoder.

We present a novel web-based framework, named Pixel-Based Edge Bundling (PBEB), for effectively and interactively visualizing large graphs. Our framework combines an image-based edge-bundling method and a parallel texture-based processing scheme, allowing us to effectively and efficiently compute edge similarities using kernel density estimation and subsequently group these edges into bundles based on their similarities. We discuss several challenges related to developing large-graph visualization on web-based platforms. To accelerate the edge bundling process and enable interactivity in web-based environments, we leverage texture-based parallel processing, a standard feature of WebGL. Our framework optimizes an end-to-end process, from bundling to rendering, enabling practical and interactive visualization of large graphs in a web-based setting. We demonstrate the superior performance of our framework by conducting comparisons with existing web-based and CUDA-based edge-bundling methods using various standard graphics cards on different devices.

Studying the growth dynamics of developing plants is of critical importance in plant sciences. The traditional methods rely on either manual measurement, which involves tedious labor work, or 2D image-based approaches, which cannot fully characterize plants in 3D. Given the advances of scanners and 3D reconstruction methods, scientists begin to pay more attention to 3D models to improve accuracy. However, existing methods mostly focus on the growth of a whole plant rather than its detailed substructures. In this paper, we have developed an end-to-end pipeline to detect the key events on both the whole plant and the specific components. Our method is achieved by building 3D models from images, segmenting individual components, and capturing traits. We implement an experiment on maizes for evaluation and successfully detect events in the process of growth.

We explore the usage of deep convolutional neural network for clustering the time steps of a spatial-temporal scientific dataset. Our approach first takes the scientific dataset as training data and trains a deep convolutional autoencoder. A low-dimensional feature space or latent space can be extracted by inferencing the encoding part of the network. As a result, each time step is transformed into a feature descriptor that can be compared with each other in the feature space. In this way, we can cluster time steps according to their feature descriptors, and each group of time steps has a similar characterization. We demonstrate the effectiveness of our approach using a real-world simulation dataset of water contamination. Multiple variables and their combinations of this dataset are fed into our approach. The trained network enables the clustering of the time steps and facilitates scientists to examine their large spatial-temporal datasets.