Joseph (Joe) Russo, Phd
Joseph (Joe) Russo, Phd Head of Research and Modelling at BASF
Co-founded by Joseph (Joe) Russo, PhD., ZedX, Inc. is a recognized R&D leader for the precision agriculture industry and became one of the leading solution providers of integrated agro-climatology business intelligence and decision support. It was also the first company to develop high resolution, and raster weather databases that are compatible with Geographic Information System (GIS) programs.

ZedX company was acquired by BASF in 2017, as part of its Crop Protection division of to strengthen the digital farming footprint of BASF and help growers take advantage of the increasing amount of big data generated in farming and beyond. In his new position, Dr. Russo is responsible for leading the development of weather, crop and pest models, decision-support algorithms, tools for precision agricultural platforms, integrated pest management systems, and data visualization tools for the agricultural industries. He is also exploring with university researchers the role artificial intelligence can play in agricultural decision making.

For close to two decades, he has been involved in the design and development of interactive, web-based, decision-support and learning services for the agricultural sector, including HighQ, AgFleet, GUI-Ads, and Weather Engine. This is coupled with over fourteen years in the design and development of information technology (IT) platforms for government and university. Some notable platforms include NAPPFAST for USDA APHIS, USA; Soybean Rust (now ipmPIPE) for USDA APHIS, CSREES, and RMA, USA; Cereal Rust Information Platform (CRIP) for the Cereal Disease Laboratory, ARS USDA, USA; PA-PIPE for the state of Pennsylvania, USA; SCOPE for the Mexican government; and iPiPE for USDA NIFA, USA. Recent platforms have mobile devise applications (apps) to collect data in the field and specialized IT tools for Extension professionals, government personnel, researchers, and educators. They also provide derivative products based on output from dynamic crop and pest models for agricultural stakeholders