Agriculture

Christopher Gilligan of the University of Cambridge in the United Kingdom will develop a data collection and analysis platform for crop diseases that uses Bayesian modelling frameworks to better integrate data from diverse sources and identifies cost-effective pest and disease control solutions for small-holder farmers. Current crop disease surveillance programs generally collect data from limited sources and lack the capacity to use the data to advise farmers how to manage any disease outbreaks.

Jan Kreuze of the International Potato Center in Peru will develop a low-cost, mobile phone-based diagnostic test for African farmers that uses artificial intelligence to quickly and accurately detect plant diseases such as cassava brown streak and banana bunchy top, which devastate crops and are threatening to spread. Accurately diagnosing plant diseases is difficult because visual symptoms can be highly variable. Artificial intelligence (AI) has shown promise for analyzing images of plants taken by mobile phone to detect diseases in low-resource settings, but it is not accurate enough.

Bruce Grieve of Manchester University in the United Kingdom will develop a low-cost, stereo-printed sensor that mimics plant leaves and stems and can detect and signal the presence of live pathogens as an early warning system to help protect crops in low-resource settings. They will demonstrate proof-of-concept of their approach in the laboratory by designing three dimensional sensors with specific patterns of cells and chemically-doped polymers to identify an ideal surface on which pathogenic fungal spores can grow and differentiate.

Jun Kameoka of Texas A&M University in the U.S. will develop multiplex, battery-less and wireless durable paper sensors for positioning under the soil in crop fields to detect the early signs of pests and diseases, and communicate the data to overhead drones via radio frequency to improve pest management. The sensor will be designed to monitor physical, biological and chemical soil conditions that are altered by plant diseases. They will test its performance in commercial garden soil with maize and sorghum plants in a vinyl house.

James Bell of Rothamsted Research in the United Kingdom will test an integrated surveillance system for the real-time detection of ground and upper atmospheric levels of the fall armyworm, which is a moth that devastates maize crops. Maize is a vital food source in Kenya but is currently largely imported and has become too expensive for most households. They propose to help local farmers grow maize by developing an early warning system for the African moth pests.

Julius Lucks of Northwestern University in the U.S. is developing a low-cost field test that can detect multiple plant pathogens and produce simple visual outputs for farmers in low-income countries to better monitor their crops. Current diagnostic field tests only detect one disease and are generally costly and difficult to use. In Phase I, they developed a sensitive, multiplexed assay that can detect multiple pathogens using biosensors and produce colorimetric outputs, and performed successful field-testing in several countries.

Pierluigi Bonello of Ohio State University in the U.S. will develop a surveillance system for crops using unmanned aerial vehicles (drones) to position sensors to help diagnose plant diseases in low-income countries. Plant diseases are usually identified first by the farmers or human scouts and then confirmed by laboratory testing. This process is inefficient and requires resources often unavailable in low-income countries, calling for alternative approaches. It is known that when a plant becomes infected, it produces specific chemicals.

Amanda Stiles of Ripple Foods, PBC, in the U.S. will produce a low-cost protein isolate upcycled from locally-sourced agricultural by-products that can be used as a nutritious food additive or standalone high-protein broth. Protein malnutrition is a major health concern in southern Asia and sub-Saharan Africa. However, protein is expensive to produce and often has a bad taste. They have developed an automated approach to identify low-cost, efficient methods to isolate plant proteins from agricultural by-products in the U.S.

Tsegaye Nega of Carleton College in the U.S. will develop methods to produce and distribute affordable nutritional food supplements made from excess, dried spent grains from the brewery process. Beer production has grown recently in Ethiopia, and a by-product, brewer's spent grain, is rich in fiber and protein and can be easily added to bread to boost its nutritional content.

Gloraia Pena of Cooperativa Multiactiva De Madres Del Valle Coomac in Colombia will implement a hybrid value chain business model to leverage collective purchasing power in a community of low-income families in Colombia to reduce the price of nutritious local foods. Current food prices are relatively high for low-income families because they buy in small volumes. They will combine collective purchasing power with a hybrid value chain model, which incorporates the needs and roles of the public and private sectors, to increase access to nutritional foods.