The past decade has witnessed the rapid development and adoption of machine and deep learning (ML & DL) methodologies in agricultural systems, showcased by great successes in applications such as ...smart crop management, smart plant breeding, smart livestock farming, precision aquaculture farming, and agricultural robotics. However, these conventional ML/DL models have certain limitations: they heavily rely on large, costly-to-acquire labeled datasets for training, require specialized expertise for development and maintenance, and are mostly tailored for specific tasks, thus lacking generalizability. Recently, large pre-trained models, also known as foundation models (FMs), have demonstrated remarkable successes in language, vision, and decision-making tasks across various domains. These models are trained on a vast amount of data from multiple domains and modalities. Once trained, they can accomplish versatile tasks with just minor fine-tuning and minimal task-specific labeled data. Despite their proven effectiveness and huge potential, there has been little exploration of applying FMs to agriculture artificial intelligence (AI). Therefore, this study aims to explore the potential of FMs in the field of smart agriculture. In particular, conceptual tools and technical background are presented to facilitate the understanding of the problem space and uncover new research directions in this field. To this end, recent FMs in the general computer science (CS) domain are reviewed, and the models are categorized into four categories: language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs. Subsequently, the process of developing agriculture FMs (AFMs) is outlined and their potential applications in smart agriculture are discussed. In addition, the unique challenges and risks associated with developing AFMs are discussed, including model training, validation, and deployment. Through this study, the advancement of AI in agriculture is explored by introducing AFMs as a promising paradigm that can significantly mitigate the reliance on extensive labeled datasets and enhance the efficiency, effectiveness, and generalization of agricultural AI systems. To facilitate further research, a well-classified and actively updated list of papers on AFMs is organized and accessible at https://github.com/JiajiaLi04/Agriculture-Foundation-Models.
•Basics of large language and foundation models.•Review of potential applications of large language and foundation models in agriculture.•Outline challenges and opportunities.
Inderjeet Parmar reveals the complex interrelations, shared mindsets, and collaborative efforts of influential public and private organizations in the building of American hegemony. Focusing on the ...involvement of the Ford, Rockefeller, and Carnegie foundations in U.S. foreign affairs, Parmar traces the transformation of America from an "isolationist" nation into the world's only superpower, all in the name of benevolent stewardship. Parmar begins in the 1920s with the establishment of these foundations and their system of top-down, elitist, scientific giving, which focused more on managing social, political, and economic change than on solving modern society's structural problems. Consulting rare documents and other archival materials, he recounts how the American intellectuals, academics, and policy makers affiliated with these organizations institutionalized such elitism, which then bled into the machinery of U.S. foreign policy and became regarded as the essence of modernity. America hoped to replace Britain in the role of global hegemon and created the necessary political, ideological, military, and institutional capacity to do so, yet far from being objective, the Ford, Rockefeller, and Carnegie foundations often advanced U.S. interests at the expense of other nations. Incorporating case studies of American philanthropy in Nigeria, Chile, and Indonesia, Parmar boldly exposes the knowledge networks underwriting American dominance in the twentieth century.
Liquefaction-induced settlement and bearing capacity failure have been reported as leading causes of damages in shallow foundations during earthquakes. Previous studies of this problem have mainly ...focused on the performance of isolated shallow foundations. In urban areas, however, foundations are generally located in close proximity. In this study, three series of centrifuge tests were conducted to investigate the effect of foundation-soil-foundation interaction (FSFI) on the seismic and post-seismic settlement of shallow foundations on saturated sand. Two rigid foundations with different surcharge loads (as heavy and light foundations) were placed with different spacing. Multiple shaking events were applied to achieve different extents of soil liquefaction. The results indicate that significant part of foundation settlement occurred before soil reconsolidation. Furthermore, the time period after shaking, wherein excess pore water pressure sustains, plays an important role in the total settlement of foundations. The acceleration responses experienced by the foundations were significantly larger than those observed in the free-field. The heavy foundation fluctuated more strongly than the light one. Moreover, adjacency considerably affected the seismic response of foundations whereas stronger acceleration response on the ground level was observed for the closer cases. The Clear asymmetric settlement was observed for the adjacent foundations. It is demonstrated that settlement of foundations not only is dependent on foundations' proximity but also is a function of shaking intensity. Influence of foundations' spacing on the generation-dissipation mechanism of excess pore water pressure (EPWP) and liquefaction extent was described by the time-dependent contours plotted by interpolation of the recorded data.
•Foundation-soil-foundation interaction on liquefiable sand is studied.•Settlement mechanisms of foundations are divided into three distinct phases.•Propagation of liquefaction extent after shaking affects foundation settlement.•Closely adjacent foundations behave like a mono-foundation.•Foundations fluctuate more strongly when located closer to each other.
Collapsible soils have been regarded to be a significant engineering concern for foundation constructions in northwest China. Given that the collapsible soils, also known as loess, cover most of ...northwest China, all buildings, structures, and utility pipelines have to deal with the collapse of loess. Despite the many empirically based methods available for evaluation of the collapse of loess, methods to assess the collapse of loess surrounded by piles are, however, rarely seen. This study proposes a simple method for evaluation of the collapse of loess surrounded by piles by taking into account both the collapsible deformation characteristics and friction at the loess–pile interface. The results of an application of the proposed method to three more loess–pile foundation worksites are presented and indicate that the proposed method can predict the settlement of loess–pile foundation satisfactorily.
