A Systems Level Approach to Crop Health
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. This has recently been made possible by studies on how environmental factors influence the emergence and behaviour of crop pests and diseases. They will use a systems approach that incorporates these new predictors along with historical data and couples them with an artificial intelligence component that learns from ground observations recorded using smartphones to improve accuracy. They will combine their existing agricultural intelligence platform and smartphone application with their prototype predictive model and test their approach with maize and cassava crops on farms across seven different counties in Kenya. The platform will produce location-specific forecasts that can be acted upon immediately by farmers.