The appearance of Fall Armyworm (FAW) (Spodoptera frugiperda) in Africa has caused much consternation: "The hungry caterpillar threatening a global food crisis", according to a headline in the Guardian newspaper. The UK Department for International Development (DFID) commissioned CABI to compile an evidence note, which was published by CABI in September 2017. This article is a summary of the evidence note, which aimed to assess the potential impact of FAW in Africa if left uncontrolled, and recommend and prioritise control options. The first confirmed reports of FAW were from West Africa in early 2016. Research to date suggests that both strains of FAW that are found in the Americas entered Africa, perhaps as stowaways on commercial aircraft, either in cargo containers or airplane holds, before subsequent widespread dispersal by the wind. The probability is high (>90%) that the introduction to Africa was from the characterised Florida strain of FAW, which is restricted to the eastern seaboard of the USA, and the Caribbean islands. Based on information from literature searches, personal communications and internet mining, as for August 2017 28 countries have confirmed the presence of FAW. A further nine countries suspect its presence, or are awaiting official confirmation of the pest in the country. Two countries (Somalia and Djibouti) have conducted surveys and not found any FAW. Using distribution and climate data collected from South America and in Ghana and Zambia, models were used to investigate the environmental (climatic) factors affecting the distribution of FAW. Results from multiple models have been combined to produce an environmental suitability index for FAW across Africa. Fall Armyworm (FAW) in Africa has the potential to cause maize yield losses in a range from 8.3 to 20.6m tonnes per annum, in the absence of any control methods, in just 12 of Africa's maize-producing countries. This represents a range of 21%–53% of the annual production of maize averaged over a three year period in these countries. The value of these potential losses is estimated at between $2,481m and $6,187m. FAW should be expected to spread throughout suitable habitats in mainland sub-Saharan Africa within the next few cropping seasons. Northern Africa and Madagascar are also at risk. As of August 2017, the pest has been confirmed present in 28 countries in Africa (compared to 12 in April 2017), with suspected presence in a further 9 countries. Control of FAW requires an integrated pest management (IPM) approach. Immediate recommendations include (i) awareness raising campaigns on FAW symptoms, early detection and control, including beneficial agronomic practices; (ii) national preparation and communication of a list of recommended, regulated pesticides and biopesticides and their appropriate application methods. Work should also start immediately to (i) assess preferred crop varieties for resistance or tolerance to FAW; (ii) introduce classical biological control agents from the Americas. A conducive policy environment should promote lower risk pest management approaches.

Fall armyworm, Spodopterafrugiperda, is a crop pest native to the Americas, which has invaded and spread throughout sub-Saharan Africa within two years. Recent estimates of 20–50% maize yield loss in Africa suggest severe impact on livelihoods. Fall armyworm is still infilling its potential range in Africa and could spread to other continents. In order to understand fall armyworm’s year-round, global, potential distribution, we used evidence of the effects of temperature and precipitation on fall armyworm life-history, combined with data on native and African distributions to construct Species Distribution Models (SDMs). We also investigated the strength of trade and transportation pathways that could carry fall armyworm beyond Africa. Up till now, fall armyworm has only invaded areas that have a climate similar to the native distribution, validating the use of climatic SDMs. The strongest climatic limits on fall armyworm’s year-round distribution are the coldest annual temperature and the amount of rain in the wet season. Much of sub-Saharan Africa can host year-round fall armyworm populations, but the likelihoods of colonising North Africa and seasonal migrations into Europe are hard to predict. South and Southeast Asia and Australia have climate conditions that would permit fall armyworm to invade. Current trade and transportation routes reveal Australia, China, India, Indonesia, Malaysia, Philippines and Thailand face high threat of fall armyworm invasions originating from Africa.

Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in unmanned aerial vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images.