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Meeting computer vision and machine learning challenges in crop phenotyping

Code: 9781801465304
Hanno Scharr, Institute of Bio- and Geosciences: Plant Sciences (IBG-2) and Institute for Advanced Simulation: Data Analytics and Machine Learning (IAS-8), Forschungszentrum Jülich, Germany; and Sotirios A. Tsaftaris, The University of Edinburgh and Alan Turing Institute, UK

Chapter synopsis: Use of imaging data has become more profound with the advent of digital cameras, the internet, and automation. With advances of computer vision, actionable information can now be extracted from images to advance the study of plants and their phenotype. In this chapter we document the experience of advancing the state of the art in developing methods that extract such information motivated and orchestrated via data challenges. We identify several examples and conclude that this can be a fruitful bridge between the communities offering problems and data with those that can offer the solutions. We offer suggestions on how to get started and conclude by providing advice for shaping data challenges of the future.

DOI: 10.19103/AS.2022.0102.11
Table of contents 1 Introduction 2 Key dimensions to consider in computer vision applications in plant phenotyping 3 Creating synergies between research communities: the Computer Vision Problems in Plant Phenotyping (CVPPP) Workshop 4 Data challenges to accelerate progress in computer vision techniques: leaf counting and segmentation 5 Recent agriculture-related computer vision challenges 6 Summary 7 Where to look for further information 8 References

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