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.

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.

Millimeter-wave (mmWave) spectrum promises high throughput links for next-generation wireless agricultural networks, which will be characterized by teams of autonomous ground vehicles, unmanned aerial vehicles (UAVs), and connected agricultural machinery. However, channel models at mmWave frequencies in agricultural environments remain elusive. Moreover, due to the dynamic crop growth behavior, agricultural field channels bear notable distinctions from urban and rural macrocellular network channels. In this work, the most extensive agricultural field experiments on the mmWave spectrum are reported and a channel model is developed to characterize the large-scale path loss, coherence bandwidth, and link quality under the effect of various environmental factors. In particular, this study investigates the effects of wind on signal-to-noise ratio, and the diffuse scattering of electromagnetic waves due to near-canopy propagation at different crop growth stages. Accordingly, (1) during the growing season, the crop canopy surface acts as a “new ground”. This new ground creates multipath components and results in a higher path loss exponent, which is correlated with the relative height between the crop canopy surface and the radios, (2) An increase of 4 m/s in gust speed results in a half-power drop (3-dB SNR degradation) due to beam misalignment and increased scattering, (3) the channel coherence bandwidth increases as the water content in the crop decreases, and (4) the beam-level spatial consistency allows for micro-mobility support for agricultural robotic applications. It is also shown that the impacts of humidity and water vapor on the mmWave channel are insignificant in the absence of rain and irrigation. Such characteristics are fundamental for designing advanced channel estimation and signal processing algorithms in advanced agricultural Internet-of-Things solutions. The extensive experiment dataset is made public for future reproducible research (https://ieeedataport.org/documents/mmwave-farm-channel-modelingwireless-agricultural-networks-broadband-millimeter-wave).

The subthreshold circuit is a practical design style for the ultralow-power applications, but its timing estimation is a challenge due to the increasing local variation effects. The delay variation of adjacent gates is not independent because of input slew variation caused by the precedent gate, so their correlation effects are difficult to model and estimate. This article proposes a semi-analytical statistical delay model considering local variation for the subthreshold region, that is, the combination of analytical and simulation-based method. First, it decorrelates the slew influence between adjacent stages by dividing delay and output slew model into fast/slow input cases and dividing delay variation model into process variation and input slew variation. Then, it can be applied into multi-PVT conditions with a one-time SPICE nominal simulation by analyzing the independence of variability and relative variability of step input gate delay variance with output load capacitance and process, voltage, and temperature (PVT). Finally, experiments are carried out for different benchmarks, processes, voltages, and temperatures (BPVTs). The average errors of variance on different BPVTs are 4.8%, 3.1%, 4.0%, and 4.7%. Compared with other analytical works, the accuracies' improvements of three metrics (variance, variability, and max delay) are 8.3×, 9.6×, and 2.7× by the mean error at all test benchmarks. Compared with industrial method LVF, it has a comparable error in max path delay and runtime, and less three orders of magnitudes than LVF in characterization time and stored data (from TB to GB) at all test benchmarks.

This paper presents techniques to include the effect of supply voltage noise on the circuit propagation delay of a digital VLSI circuit. The proposed methods rely on an input-independent approach to calculate the logic gate's worst-case power supply noise. A quasi-static timing analysis is then applied to derive a tight upper-bound on the delay for a selected path with power supply noise effects. This upper-bound can be further reduced by considering the logic constraints and dependencies in the circuit. Experimental results for ISCAS-85 benchmark circuits are presented using the techniques described in the paper. HSPICE simulation results are also used to validate our work.

In this paper, we present simulation techniques to estimate the worst-case voltage variation using an RC model for the power distribution network. Pattern independent maximum envelope currents are used as a periodic input for performing the frequency-domain steady-state simulation of the linear RC circuit to evaluate the worst-case instantaneous voltage drop for the RC power distribution networks. The proposed technique unlike existing techniques, is guaranteed to give the maximum voltage drop at nodes in the RC power distribution network. We present experimental results to compare the frequency-domain and time-domain simulation techniques for estimating the maximum instantaneous voltage drop. We also present frequency domain sensitivity analysis based decoupling capacitance placement for reducing the voltage variation in the power distribution network. Experimental results on circuits extracted from layout are presented to validate the simulation and optimization techniques.