The Mid-Century Showroom: Knoll, Herman Miller and the Retail Exhibition of Modern American Design

Although occasionally illustrated and referenced in contemporary histories of modern furniture and design, there is surprisingly little critical discussion or consideration of the role of the showroom in the promotion and dissemination of modern design during the mid-twentieth century. In these years, when the American lifestyle was popularly articulated and forcefully propagandized, the furniture showroom served as a principle site of professional and public indoctrination. Appropriating display techniques from modern exhibition design to showcase the American lifestyle as an abstracted, spatially integrated art form, the showroom provided an unencumbered landscape ideally suited to camera’s lens and the public’s imagination. Leading modern American furniture manufacturers, such as Herman Miller and Knoll Associates collaborated with major cultural institutions as well as department stores and retailers to maximize exposure and consumer demand for their products. Through such integrated marketing and merchandising strategies, showrooms also contributed to the broader social project to educate American consumers about modern design and the advantages of modern living. Related to the many model home programs and “good design” exhibitions of the 1950s, the furniture showroom occupies a unique place within the history and discourse of the postwar era. The peculiarities of the furniture showroom and its position as a point of intersection between the trade and the consumer, the commercial and the cultural, and the aesthetic and the ideological form the focus of this study.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.