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Birte Boelt

Digitalization in herbage grass seed production and research: Opportunities and challenges

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearch

Digital herbage seed production and research offer new possibilities but also new challenges. Technological, software, and algorithm development proceeds extremely rapidly, while the development of decision support systems and related agronomic and biological interpretation lags. These challenges must not prevent us from exploring new technology and methods in herbage seed production and research. We must recognize, however, that increasing the utilization of applied and available nutrients through the use of sensors, local weather data, and predictions, in combination with application algorithms, may not necessarily increase farmer revenue or reduce negative environmental impacts. Advanced field phenotyping holds great potential and is receiving a great deal of attention, based on publication frequency, and breeders will gain new knowledge from these technologies and methods. The use of advanced phenotyping in combination with crop models is an interesting area that undoubtedly will increase our basic knowledge of plant development and performance, which is necessary in order to better understand the interactions between nutrient utilization, optimal application timing for plant growth regulation, weed control, and other field operations. The conclusion is that digital herbage seed production and research are still in the early development stage.
Original languageEnglish
Title of host publicationProceedings of the Tenth International Herbage Seed Group Conference
EditorsNicole P Anderson
Number of pages4
Publication year12 May 2019
Publication statusPublished - 12 May 2019
Event10th International Herbage Seed Group Conference - Oregon State University, Corvallis, United States
Duration: 12 May 201919 May 2019


Conference10th International Herbage Seed Group Conference
LocationOregon State University
LandUnited States

    Research areas

  • digital farming, image analysis, sensor systems, machine learning, phenotyping

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