This chapter reviews advances in the use of robotics in crop phenotyping. It first highlights the role of robotics in phenotyping, then moves on to discuss three forms of dimensional imaging and analysis: two dimensional (2D), 3D and 4D. A section is dedicated to each. The chapter also provides two case studies, the first focuses on models to detect abiotic stress in corn plants using spectral reflectance and hyperspectral images of plant leaves. The second case study draws attention to biotic stress and compares leaf point spectra and whole plant hyperspectral images in the early detection of Fusarium infection in corn plants.

M. Wattad, Technion - Israel Institute of Technology, Israel; V. Alchanatis, Volcani Institute - Agricultural Research Organization, Israel; Y. Edan, Ben-Gurion University of the Negev, Israel; S. Shriki and T. Sandovsky, Volcani Institute - Agricultural Research Organization and Ben-Gurion University of the Negev, Israel; and S. Filin, Technion - Israel Institute of Technology, Israel

• 1 Introduction
• 2 The role of robotics in phenotyping
• 3 Two-dimensional plant imaging and analysis for phenotyping
• 4 Three-dimensional plant imaging and analysis for phenotyping
• 5 Four-dimensional plant imaging and analysis for phenotyping
• 6 Case study: single view 3D reconstruction and analysis for phenotyping
• 7 Case study: abiotic stress
• 8 Case study: biotic stress
• 9 Conclusion and future perspectives
• 10 Where to look for further information
• 11 References