Agriculture ICT and Technology

Hamed Alemohammad of Open Imagery Network Inc. in the U.S. and Ernest Mwebaze of Google AI Research Center in Ghana will generate synthetic imaging data to train machine learning algorithms to better interpret satellite images in low-resource settings to monitor crops and increase food security. The increase in global satellite observations at different spatial and temporal scales has led to the development of sophisticated analytical methods such as machine learning for a variety of applications.

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.

Joyce Nakatumba-Nabende of Makerere University in Uganda will use artificial intelligence to mine data from local village radio stations to generate timely data on crop pests and disease in sub-Saharan Africa. Crop loss due to pests and disease threatens the economic survival of smallholder farmers, and access to surveillance data is critically important yet often unaffordable. Local radio shows are a powerful source of information flow in rural African villages: they cover topics including politics, policy, climate, and social circumstances, in addition to crop concerns.

David Hughes of Pennsylvania State University, John Corbett of aWhere, and Rhiannan Price of DigitalGlobe, in the U.S. will develop a software platform comprising prediction algorithms that leverage artificial intelligence to predict where and when plant diseases and pests will occur from weather and satellite data to alert farmers to check their crops. Pests and diseases are moving targets, however most current surveillance methods monitor only their presence or absence. Predicting when and where they are likely to occur would be more valuable for preventing them.

Cambria Finegold, Richard Shaw and Roger Day of the Centre for Agriculture and Bioscience International in collaboration with Katherine Denby of the University of York and Sarah Gurr of the University of Exeter all in the United Kingdom, will design a platform - GBCrop - to collect, analyze and disseminate data on the global impact of crop pests and disease. The fact that 40% of crops are lost to pests impacts both the global food supply and local economies. Despite this, little is known about why and how crop pests and diseases occur.

In this ICT-based pilot project, Digital Education, tested the impact of a combination of ICT and Participatory Learning Action (PLA) approaches to improve women's knowledge of nutrition in 30 villages. They promoted the dissemination of a series of nutrition-specific participatory videos to address nutrition-specific behaviors, locally feasible solutions as well as expenditure patterns to improve maternal and child diet quality.