Processing and Production

Sunday Ekesi of the International Centre of Insect Physiology and Ecology in Kenya and Josh Tewskbury of Future Earth in the U.S. will model the effects of climate change on major food crops and their insect pests to better forecast crop yields and inform intervention strategies. The changing climate will likely have a multitude of effects on both insect-pest populations, by affecting their size and activity, and on crop physiology, which together will affect yield.

Matteo Rinaldi of Northeastern University in the U.S. will develop a miniaturized, maintenance-free chemical sensor that can detect specific volatile organic chemical vapors released from diseased crops as an effective surveillance system suitable for low-resource settings. Manual surveillance is time-consuming and requires prior knowledge of disease symptoms. Automated, sensor-based crop surveillance is far more effective, but relatively expensive, and the sensors constantly consume power, making them unsuitable for low-resource settings.

Menale Kassie of the International Centre of Insect Physiology and Ecology in Kenya along with Ram Fishman and Opher Mendelsohn from Tel Aviv University in Israel will take a community-based crowdsourcing approach to crop protection of smallholder farms in low-resource settings by developing a simple software platform for basic feature phones to monitor pest incidence. Human-based monitoring of crops is the most accurate way to identify pests, but there are too few public monitoring agents in low-resource settings, leaving the majority of farms unprotected.

David Hughes of Pennsylvania State University in the U.S. is leveraging real-time, high-resolution satellite imagery of smallholder farms along with artificial intelligence to automatically detect crop pests and diseases in Africa. In Phase I, together with Nita Bharti also of Penn State University in the U.S.

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.