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Publications

NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.

2024

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Abstract

Recurrent climate-driven disturbances impact on the health of European forests that reacted with increased tree dieback and mortality over the course of the last four decades. There is therefore large interest in predicting and understanding the fate and survival of forests under climate change. Forest conditions are monitored within the pan-European ICP Forests programme (UN-ECE International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests) since the 1980s, with tree crown defoliation being the most widely used parameter. Defoliation is not a cause-specific indicator of tree health and vitality, and there is a need to connect defoliation levels with the physiological functioning of trees. The physiological responses connected to tree crown defoliation are species-specific and concern, among others, water relations, photosynthesis and carbon metabolism, growth, and mineral nutrients of leaves. The indicators to measure physiological variables in forest monitoring programs must be easy to apply in the field with current state-of-the-art technologies, be replicable, inexpensive, time efficient and regulated by ad hoc protocols. The ultimate purpose is to provide data to feed process-based models to predict mortality and threats in forests due to climate change. This study reviews the problems and perspectives connected to the realization of a systematic assessment of physiological variables and proposes a set of indicators suitable for future application in forest monitoring programs.

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Abstract

Common scab (CS) is a major bacterial disease causing lesions on potato tubers, degrading their appearance and reducing their market value. To accurately grade scab-infected potato tubers, this study introduces “ScabyNet”, an image processing approach combining color-morphology analysis with deep learning techniques. ScabyNet estimates tuber quality traits and accurately detects and quantifies CS severity levels from color images. It is presented as a standalone application with a graphical user interface comprising two main modules. One module identifies and separates tubers on images and estimates quality-related morphological features. In addition, it enables the extraction of tubers as standard tiles for the deep-learning module. The deep-learning module detects and quantifies the scab infection into five severity classes related to the relative infected area. The analysis was performed on a dataset of 7154 images of individual tiles collected from field and glasshouse experiments. Combining the two modules yields essential parameters for quality and disease inspection. The first module simplifies imaging by replacing the region proposal step of instance segmentation networks. Furthermore, the approach is an operational tool for an affordable phenotyping system that selects scab-resistant genotypes while maintaining their market standards.