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. Their system will integrate an entomological radar to detect moths flying up to 1,200 metres overhead, with twenty ground traps covering 7,000 km2 that transmit data to a central institute, and a smart-phone application for workers and growers that automatically detects the caterpillars and moths. They will optimize the equipment and software to detect the specific moth species and test it in a region of Western Kenya over one year. Their system will also reveal details of seasonal moth migrations, ground spread, and crop growth to help develop effective pest management strategies.

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. Incorporated sensor cells will be designed to detect the live pathogens and produce a detectable response, such as a visible density change, and results can be stored locally or transmitted wirelessly. They will test different sensor designs for the detection of rust pathogens in wheat. Their approach can be adapted to detect multiple pathogens simultaneously, including viruses, as well as for human and livestock pathogens, and when deployed in the field can ultimately be linked to national surveillance systems.

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. By integrating a wider variety of data, including meteorological data, and grower and market behaviour such as household nutrition, their approach can predict much broader consequences of crop diseases on individual households and thereby provide more valuable solutions. They will focus on pests and diseases of maize, wheat, and cassava in East Africa and pilot test their SMS and smart phone platform by holding training workshops for participants, testing data analytics and validating the results.

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. They will develop a low-energy sensor-based monitoring system by exploiting a recently developed technology that comprises a micromechanical switch made of two cantilever beams. One of the beams will be coated with a polymer sensitive to the plant-based chemical and exposed to the environment. In the presence of that chemical, the beam undergoes a change in mechanical stress, causing it to bend and make contact with the second beam to trigger the switch. They will develop the microswitch-based chemical sensors, integrate them with a low-power long-range wireless module to signal pest detection, and test the performance of prototypes in the laboratory.