Agriculture

Galaletsang Tsontswane of Congretype in South Africa will design low-cost, solar-powered insect detection traps equipped with wireless sensors to capture images of insects and transmit them to a central control point to improve rural crop surveillance in developing countries. Crop loss due to pest infestation negatively impacts both food supply and local economies, while rural farmers in developing countries lack resources to monitor crop disease and infestations and are unable to respond before substantial loss occurs.

Benedict Mongula of the University of Dar es Salaam in Tanzania will analyze two apparently conflicting national agricultural policies centered on either large-scale agriculture or smallholder farmers and determine how to combine them to benefit all stakeholders for inclusive agricultural transformation. Agriculture is central to the Tanzanian economy, yet its impact is limited by a lack of infrastructure, education, and market access.

William Kunin of the University of Leeds in the United Kingdom will develop methods to monitor agricultural pest outbreaks in Africa using data from dual-polarization weather radar. Pest infestation is responsible for up to 50% of pre-harvest crop loss in Central Africa, and control depends on the ability to monitor local pest outbreaks and movement over large areas – a difficult and expensive task. Sophisticated dual polarization Doppler weather radar is designed to detect airborne objects like rain and hail.

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.

Alpha Sennon of WHYFARM in Trinidad and Tobago, along with Wainella Isaacs, Candace Charles-Sennon, Luke Smith, George Caesar, Akinola Sennon and their partners at TECH4Agri, will engage young people, who are the future feeders of 2050, in agriculture, and develop their knowledge and skills so that they can promote sustainable agriculture and improve food security in Trinidad and Tobago. They will implement four related projects in which participants can win cash prizes.

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.