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.

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.

Deep Neural Networks (DNNs) have performed admirably in classification tasks. However, the characterization of their classification uncertainties, required for certain applications, has been lacking. In this work, we investigate the issue by assessing DNNs’ ability to estimate conditional probabilities and propose a framework for systematic uncertainty characterization. Denoting the input sample as x and the category as y, the classification task of assigning a category y to a given input x can be reduced to the task of estimating the conditional probabilities p(y|x), as approximated by the DNN at its last layer using the softmax function. Since softmax yields a vector whose elements all fall in the interval (0, 1) and sum to 1, it suggests a probabilistic interpretation to the DNN’s outcome. Using synthetic and real-world datasets, we look into the impact of various factors, e.g., probability density f(x) and inter-categorical sparsity, on the precision of DNNs’ estimations of p(y|x), and find that the likelihood probability density and the inter-categorical sparsity have greater impacts than the prior probability to DNNs’ classification uncertainty.

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.

Nowadays, floating-point temporal-spatial datasets are routinely generated from scientific observational apparatuses or computer simulations at an unprecedented pace. The sheer amount of these large volumetric datasets on the order of terabytes or petabytes consume massive resources in terms of bandwidth, storage and computational power. On the other hand, scientists, equipped with low-end post-analysis machines, often find it impossible to visualize and analyze these massive datasets with such limited resources in hand, not to mention their ultimate goal of real time analysis and visualization. To solve this discrepancy, a compact data representation has to be generated and a trade-off between resource consumption and analytical precision has to be found. There are many existing volumetric representation generating methods, almost all of which adopts some kind of hand-engineered heuristics to extract the effective portion of the datasets. However, the trade-off between resource consumption and analytical quality could not be well established due to the introduction of hand-engineered heuristics. In this paper, we present a deep learning based method that can adaptively capture the inherently complicated dynamics of temporal-spatial volumetric datasets without introducing any hand engineered features. We train an autoencoder based neural network with quantization and adaptation. Compared with existing methods, our method could learn data representation at a much lower compressed/uncompressed rate while preserving the details of original datasets. Also, our method could adapt with different data distribution and conduct compression and decompression in real time. Through extensive experiments, we show the effectiveness and efficiency of our approach over existing methods.

Analysis of hyperspectral images is of great importance in many scientific disciplines. Obtaining the spectral and spatial information simultaneously from time-varying hyperspectral images is a challenging task due to their high dimensionality. In this paper, we design an interface that allows users to study hyperspectral images interactively and obtain spectral features and enhanced images at the same time. The image fusion results change dynamically with the regions of interest selected by users and convey both the spatial and spectral information. We show the usefulness of our approach using time-varying hyperspectral plant images. We compare our method with existing hyperspectral image analysis techniques. Our evaluation indicates that our interface can help users determine important bands, identify regions of interest, and generate image fusion results for time-varying hyperspectral plant images.

We have devised and implemented a key technology, SpatioTemporal Adaptive-Resolution Encoding (STARE), in an array database management system, i.e. SciDB, to achieve unparalleled variety scaling for Big Earth Data, enabling rapid-response visual analytics. STARE not only serves as a unifying data representation homogenizing diverse varieties of Earth Science Datasets, but also supports spatiotemporal data placement alignment of these datasets to optimize a major class of Earth Science data analyses, i.e. those requiring spatiotemporal coincidence. Using STARE, we tailor a data partitioning and distribution strategy for the data access patterns of our scientific analysis, leading to optimal use of distributed resources. With STARE, rapid-response visual analytics are made possible through a high-level query interface, allowing geoscientists to perform data exploration visually, intuitively and interactively. We envision a system based on these innovations to relieve geoscientists of most laborious data management chores so that they may focus better on scientific issues and investigations. A significant boost in scientific productivity may thus be expected. We demonstrate these advantages with a prototypical system including comparisons to alternatives.