Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.
Fall Armyworm first appeared in Africa in 2016, in West Africa, and then rapidly spread across all countries in sub-Saharan Africa in 2017, infecting millions of hectares of maize, and threatening the food security of more than 300 million people. Many African farmers might have heard about Fall Armyworm but are seeing it for the first time, and are often unable to recognize it or unsure of what they are facing. With the new application, they can hold the phone next to an infested plant, and Nuru can immediately confirm if Fall Armyworm has caused the damage. Nuru is an app that uses cutting-edge technologies involving machine learning and artificial intelligence. It runs inside a standard Android phone and can work also offline.
Food security is threatened by many things. In some regions, climate variability causes droughts that make vital resources scarce. In others, political turmoil creates logistical blockades for farming, harvesting, and shipping produce. But, practically everywhere, plant disease can wipe out entire crops with little warning. A team of researchers at Pennsylvania State University and the École Polytechnique Fédérale de Lausanne, Switzerland have turned the keen eye of artificial intelligence toward agriculture, using deep learning algorithms to help detect crop disease before it spreads.