In vivo spectroscopy and machine learning for the early detection and classification of different stresses in apple trees.

The use of in vivo spectroscopy to detect plant stress in its early stages has the potential to enhance food safety and reduce the need for plant protection products. However, differentiating between various stress types before symptoms appear remains poorly studied. In this study, we investigated the potential of Vis-NIR spectroscopy to differentiate between stress types in apple trees (Malus x domestica Borkh.) exposed to apple scab, waterlogging, and herbicides in a greenhouse. Using a spectroradiometer, we collected spectral signatures of leaves still attached to the tree and utilized machine learning techniques to develop predictive models for detecting stress presence and classifying stress type as early as 1-5 days after exposure. Our findings suggest that changes in spectral reflectance at multiple regions accurately differentiate various types of plant stress on apple trees. Our models were highly accurate (accuracies between 0.94 and 1) when detecting the general presence of stress at an early stage. The wavelengths important for classification relate to photosynthesis via pigment functioning (684 nm) and leaf water (~ 1800-1900 nm), which may be associated with altered gas exchange as a short-term stress response. Overall, our study demonstrates the potential of spectral technology and machine learning for early diagnosis of plant stress, which could lead to reduced environmental burden through optimizing resource utilization in agriculture.

Identification of spectral ranges that contribute to phytoplasma detection in apple trees - A step towards an on-site method.

'Candidatus Phytoplasma mali' is the bacterial agent associated with Apple Proliferation, a disease that causes high economic losses in affected commercial apple growing regions. The identification of the disease is carried out by visual inspection performed by skilled professionals in the orchards. To confirm an infection, costly molecular laboratory methods must be applied. Furthermore, both methods are very time-consuming. Here, we analysed the potential of a non-destructive method using in-field measurements to differentiate infected from non-infected apple trees (Malus domestica) based on spectral signatures of fresh leaves. By using multivariate statistics, we were able to distinguish infected from non-infected trees and identified the wavelengths relevant for the differentiation. Factors affecting the differentiation performance were the sampling date and bacterial colonization behaviour.

Drones reduce the treatment-free interval in search and rescue operations with telemedical support - A randomized controlled trial.

INTRODUCTION: Response to medical incidents in mountainous areas is delayed due to the remote and challenging terrain. Drones could assist in a quicker search for patients and can facilitate earlier treatment through delivery of medical equipment. We aim to assess the effects of using drones in search and rescue (SAR) operations in challenging terrain. We hypothesize that drones can reduce the search time and treatment-free interval of patients by delivering an emergency kit and telemedical support. METHODS: In this randomized controlled trial with a cross-over design two methods of searching for and initiating treatment of a patient were compared. The primary outcome was a comparison of the times for locating a patient through visual contact and starting treatment on-site between the drone-assisted intervention arm and the conventional ground-rescue control arm. A linear mixed model (LMM) was used to evaluate the effect of using a drone on search and start of treatment times. RESULTS: Twenty-four SAR missions, performed by six SAR teams each with four team members, were analyzed. The mean time to locate the patient was 14.6 min (95% CI 11.3-17.9) in the drone-assisted intervention arm and 20.6 min (95% CI 17.3-23.9) in the control arm. The mean time to start treatment was 15.7 min (95% CI 12.4-19.0) in the drone-assisted arm and 22.4 min (95% CI 19.1-25.7) in the control arm (p < 0.01 for both comparisons). CONCLUSION: The successful use of drones in SAR operations leads to a reduction in search time and treatment-free interval of patients in challenging terrain, which could improve outcomes in patients suffering from traumatic injuries, the most commonly occurring incident requiring mountain rescue operations.

Vegetation traits of pre-Alpine grasslands in southern Germany.

The data set contains information on aboveground vegetation traits of > 100 georeferenced locations within ten temperate pre-Alpine grassland plots in southern Germany. The grasslands were sampled in April 2018 for the following traits: bulk canopy height; weight of fresh and dry biomass; dry weight percentage of the plant functional types (PFT) non-green vegetation, legumes, non-leguminous forbs, and graminoids; total green area index (GAI) and PFT-specific GAI; plant water content; plant carbon and nitrogen content (community values and PFT-specific values); as well as leaf mass per area (LMA) of PFT. In addition, a species specific inventory of the plots was conducted in June 2020 and provides plot-level information on grassland type and plant species composition. The data set was obtained within the framework of the SUSALPS project ("Sustainable use of alpine and pre-alpine grassland soils in a changing climate"; https://www.susalps.de/ ) to provide in-situ data for the calibration and validation of remote sensing based models to estimate grassland traits.