Keynote Speaker


Professor Seifedine Kadry

Noroff Education AS, Norway
Title: Computer Vision based Plants Phenotyping: Challenges, Opportunities, and Perpectives

The increasing demand for food production due to the growing population is raising the need for more productive plant environments. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to manually look after the individual plant component traits. Plant breeders need computer vision-based plant monitoring systems to analyze a more productive, suitable environment, quantitative analysis, geometric analysis, and yield rate analysis. Many of the data collection methods according to the needs have been used by plant breeders. In the presented review, most of them are discussed with their corresponding challenges and limitations.

Furthermore, the traditional approaches of segmentation and classifications of plant phenotyping are also discussed. The data limitation problems and their currently adapted solutions in the computer vision aspect are highlighted that somehow solve the problem but are not genuine. The available data sets and current issues are enlightened. The presented study covers the data collection to classification tasks.


He has a bachelor’s degree in 1999 from Lebanese University, an MS degree in 2002 from Reims University (France) and EPFL (Lausanne), Ph.D. in 2007 from Blaise Pascal University (France), an HDR degree in 2017 from Rouen University (France). His research currently focuses on Data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, Data Science, and Engineering Tech. He is a full professor of data science at Noroff University College, Norway. He is an IET Fellow.