Andrew Leakey, Jiayang (Kevin) Xie and their colleagues developed an improved method for analyzing features of plant leaves that contribute to water-use efficiency in crops like corn, sorghum (pictured) and Setaria. They used advanced statistical approaches to identify regions of the genome and lists of genes that contribute to these traits. Scientists have developed and deployed a series of new imaging and machine learning tools to discover attributes that contribute to water-use efficiency in crop plants during photosynthesis and to reveal the genetic basis of variation in those traits. The findings are described in a series of four research papers led by University of Illinois Urbana-Champaign graduate students Jiayang (Kevin) Xie and Parthiban Prakash, and postdoctoral researchers John Ferguson, Samuel Fernandes and Charles Pignon. The goal is to breed or engineer crops that are better at conserving water without sacrificing yield, said Andrew Leakey, a professor of plant biology and of crop sciences at the University of Illinois Urbana-Champaign, who directed the research.

Object detection is a computer vision field that has applications in several contexts ranging from biomedicine and agriculture to security. In the last years, several deep learning techniques have greatly improved object detection models. Among those techniques, we can highlight the YOLO approach, that allows the construction of accurate models that can be employed in real-time applications. However, as most deep learning techniques, YOLO has a steep learning curve and creating models using this approach might be challenging for non-expert users. In this work, we tackle this problem by constructing a suite of Jupyter notebooks that democratizes the construction of object detection models using YOLO. The suitability of our approach has been proven with a dataset of stomata images where we have achieved a mAP of 90.91%.

A silicon valley startup has developed an AI jacket with slits that automatically open or close to adjust your body temperature. The slits on the jacket help make it more breathable if, for example, you're skiing or just feeling hot on a stuffy train car during your commute. The slits are on the front and back of the jacket and can be operated manually - with the AI eventually learning your temperature preference and adjusting the slits automatically. The jacket has slits on the front and back, which open and close depending on your body temperature. The company, Omius, based in Menlo Park, California, modeled the jacket after the stomata of plants - the tiny pores that allow plants to let gas in and out. The jacket has slits on the front and back, which open and close depending on your body temperature.