In the talk, the application of bioimpedance for fruit quality characterization will be presented, by first introducing a theoretical background, and then providing case-studies from our work on the subject. Specifically, our portable impedance analyzer (the FruitMeter), its integration with printed electrodes and ad-hoc developed machine learning classification algorithms for the on-plant ripeness discrimination will be presented. In addition, an overview of applications for fruit post-harvest quality and plant health monitoring will be given.