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Toward Digital and Image-Based Phenotyping

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Phenomics in Crop Plants: Trends, Options and Limitations

Abstract

Manually performed measurements in field phenotyping are labor- and time-consuming, often destructive and not objective. Moreover, the complexity and variability of crop plants under field conditions require high-resolution data and filtering. As a consequence, there is a need for spatially and temporally differentiated data, objective data acquisition, and high-throughput technologies. Image-based systems, selective on morphological and spectral crop characteristics, are adequate sensors for further interpretation of raw data in terms of crop properties. In particular multi-sensor and data fusion has a potential to compensate the varying influences of sunlight, dust, moisture, or uneven land in the field. Due to the high-resolution data of image-based systems – such as digital color cameras, spectral imaging, laser scanning devices, or 3D cameras – detailed crop properties have become available, even individual plant phenotyping is an option. Autonomous field robots have a high potential for field phenotyping as well as new sensor technologies and virtual phenotyping. Data management is of relevance for field phenotyping, starting from storing the large amounts of raw data up to artificial intelligence algorithms for trait determination. Interdisciplinary cooperation is crucial for the implementation of digital phenotyping into practice.

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Acknowledgments

The research project “BoniRob” was funded by the Ministry of Food, Agriculture and Consumer Protection (BMELV) and by the Federal Office for Agriculture and Food (BLE). The research project “BreedVision” was funded by the German Federal Ministry of Education and Research (BMBF).

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Authors and Affiliations

  1. COALA – Competence Center of Applied Agricultural Engineering, University of Applied Sciences Osnabrück, Albrechstr. 30, 49076, Osnabrück, Germany

    Arno Ruckelshausen & Lucas Busemeyer

Authors
  1. Arno Ruckelshausen
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  2. Lucas Busemeyer
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Corresponding author

Correspondence to Arno Ruckelshausen .

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Editors and Affiliations

  1. Division of Crop Improvement, ICAR - Indian Institute of Pulses Research, Kalyanpur, Kanpur, Uttar Pradesh, India

    Jitendra Kumar

  2. Division of Crop Improvement, ICAR - Indian Institute of Pulses Research, Kalyanpur, Kanpur, Uttar Pradesh, India

    Aditya Pratap

  3. Rabat - Instituts, International Center for Agricultural Research in the Dry Areas, Rabat, Morocco

    Shiv Kumar

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Ruckelshausen, A., Busemeyer, L. (2015). Toward Digital and Image-Based Phenotyping. In: Kumar, J., Pratap, A., Kumar, S. (eds) Phenomics in Crop Plants: Trends, Options and Limitations. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2226-2_4

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