In agricultural production, many decisions rely on visual inspection by humans, such as assessing crop growth conditions and identifying diseases and pests. The Recognition and Estimation Research Group aims to contribute to improved efficiency and labor saving in agricultural production by transforming such human visual judgments into image data and automating and advancing them through AI-based image recognition and analysis technologies.
The group conducts research and development on image recognition-based technologies for pest and disease identification, crop growth diagnosis, and soil condition estimation. In addition, we are exploring novel pest control technologies using high-power laser beams as an alternative to chemical pesticides.
The image data we handle are not limited to digital camera images, but also include data acquired from a wide range of devices, such as Earth observation satellites, drones, vehicle-mounted cameras, and smartphones. These technologies are applied across a broad spectrum of agricultural research fields, including crop production, pest and disease control, livestock farming, and fruit tree cultivation.
