Agriculture

Peter Wagstaff of Self Help Africa in Ireland will build an advanced machine learning algorithm that automatically analyzes high-resolution satellite images for near real-time, low-cost detection of crop pests and diseases across wide, varied landscapes. Current detection methods are either resource- or cost-intensive and limited in their ability to provide up-to-date information across large and complex geographic areas. Crop pests and diseases can alter leaf color and expose soil, which can be detected by very high-resolution satellite imaging.

Ritvik Sahajpal of the University of Maryland College Park in the U.S. will develop an early warning system for low-income countries that predicts the threat to crops from pests and diseases by combining machine learning and crop pest modelling with freely available earth observation data. Existing monitoring systems allow farmers to share data on pest incidence to ensure the timely and limited use of treatments. This maximizes crop yield while minimizing cost and environmental damage.

Hanseup Kim of the University of Utah in the U.S. will develop small, ultra-low power, chemical sensors that can be distributed around farms to help detect crop diseases in low-resource settings. Plants under attack from pests and diseases release low levels of volatile organic compounds that could be used as an early warning system to reduce crop losses, which can be substantial.

William Martin of the International Food Policy Research Institute in the U.S. will combine existing, high-quality survey data collected from individual households in rural Ethiopia and Nigeria with agricultural information from the Global Agro-Ecological Zone (GAEZ) database to model the impact of specific investments on poor communities to better inform policy decisions. National Agricultural Investment Plans (NAIPS) shape policy development by outlining investments required to stimulate growth in Africa through transformation of the agricultural sector.

Molly Brown of Syngenta Foundation for Sustainable Agriculture in the U.S. will develop an early warning system for crop diseases for rural farmers in Africa by gathering data using existing infrastructure and mobile tools from commercial partners and applying machine learning pest models. Insect pests cause almost half of crop losses in Africa each year, which impacts both food supply and the economy.

Firat Guder and Tony Cass of Imperial College London in the United Kingdom along with George Mahuku and James Legg at the International Institute of Tropical Agriculture in Tanzania are developing a low-cost, disposable electrochemical lateral flow assay for smartphones to rapidly detect crop viruses in the field and enable broad crop disease surveillance in low-income regions. Most diagnostic tests are laboratory-based, expensive, and slow.

Christine Lamanna and Todd Rosenstock of the World Agroforestry Centre in Kenya will develop a strategy that combines local knowledge and a Bayesian network model to prioritize agricultural policy using Tanzania’s Agriculture and Food Security Investment Plan as a case study. Agriculture is responsible for nearly one third of Africa’s gross domestic product, yet productivity suffers from limited infrastructure and lack of access to markets and financing.

Amit Lal of Geegah LLC in the U.S. will develop battery-powered ultrasonic imagers to collect and wirelessly transmit high-resolution images of soil and airborne pests for the early detection of crop threats across large farming areas in rural Africa. Crop losses due to pest infestation negatively impact both food security and local economies. Damage caused by nematodes is particularly difficult to detect because the symptoms visible above ground are not unique and are often incorrectly attributed to deficiencies in soil nutrients or moisture.

Kindie Tesfaye-Fantaye of the International Maize and Wheat Improvement Center in Mexico will develop a computational model that incorporates the variable characteristics of households and farms to better predict the outcomes of agricultural interventions in Ethiopia in order to inform policy choices. Agriculture is central to the Ethiopian economy; it accounts for almost 50% of the gross domestic product and 80% of total employment, yet the industry struggles with limited infrastructure and environmental challenges.

Melanie Bannister-Tyrrell of Ausvet in Australia will create an SMS-based communication system for farmers in Kenya to anonymously report crop disease and pest infestations and generate surveillance data to minimize crop loss. Pest infestation and disease cause substantial crop losses each year. In many low-income countries, farmers do not report disease to local agricultural authorities because they fear their crops will be destroyed without compensation. Yet information on the presence and spread of pests is needed to inform decisions on planting and control measures.