Browsing by Subject "Pathogen detection"

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Classifying early-stage soybean fungal diseases on hyperspectral images using convolutional neural networks
(2025) Hsiao, Chieh Fu; Feyrer, Georg; Stein, Anthony
Using convolutional neural networks (CNNs) to detect plant diseases has proven to reach high accuracy in the classification of infected and non-infected plant images. However, most of the existing researches are based on RGB images due to the availability and the comparably low cost of image collection. The limited spectral information restricts the detectability of plant diseases, especially in the early stage where often symptoms of pathogen infection have not yet become visible. To this end, in this study, hyperspectral imaging (HSI) data are combined with deep learning models to test the classification ability of two soybean fungal diseases: Asian soybean rust (Phakopsora pachyhizi) and soybean stem rust (Sclerotinia scleroriorum). Different CNNs employing 2D, 3D convolution, and hybrid approaches are compared. The influences of the depth of the convolutional layer and the regularization techniques are also discussed. Besides, image augmentation methods are investigated to overcome the problem of data scarcity. The results indicate the 6-convolutional-layer depth hybrid model to have the best capacity in classifying Asian soybean rust in the early-mid to mid-late stage when there are over 2 % visible symptoms but a limited detectability in the early stages when there are below 2 % visible symptoms on leaves. On the other hand, the optimized CNN model shows a limited capability to detect both diseases when there are no visible symptoms observable. Overall, this study suggests a hybrid 2D-3D convolutional model with augmentation and regularization methods has a high potential in the early detection of fungal diseases. This research is expected to contribute to a new cropping system that vastly reduces the chemical-synthesis plant protection products, where a continuous pathogen disease monitoring plays a key to manage the crop stands.
Unwanted souvenirs - import routes and pathogen detection of the non-endemic tick Rhipicephalus sanguineus s.l. in Germany
(2025) Fachet-Lehmann, Katrin; Lindau, Alexander; Mackenstedt, Ute
Ticks of the Genus Rhipicephalus occur worldwide. Especially members of Rh. sanguineus s.l. are primarily associated with dogs. As unwanted souvenirs, they are introduced by dogs into non-endemic areas such as Germany, where they can establish and reproduce indoors . A citizen-science study was conducted between 2019 and 2024, asking interested citizens to report tick infestations and send in travel related tick findings which were associated with dogs and were focused on Rhipicephalus species. Tick species were identified using the 16S rRNA gene and tested for pathogens associated with the genus Rhipicephalus . In addition, each tick introduction was considered as a case and categorized and analyzed individually. During the study period, 44 cases were reported. In 17 cases tick specimens were identified as Rh. sanguineus s.s., Rh. rutilus , Rh. linnaei, Rh. turanicus , and Rh. haemaphysaloides which were imported from other European countries and Sri Lanka. Neither Hepatozoon canis , Ehrlichia canis nor Babesia vogeli were detected in any of the 780 received specimens. In contrast, Rickettsia spp. was detected in 50 specimens from six independent cases, with Rickettsia massiliae being identified in 44 specimens. The import of dogs from abroad as well as travel with dogs lacking appropriate tick prophylaxis are responsible for more than 80% of cases. This Project highlights the risk of introductions of non-endemic tick species. Also, professionals such as veterinarians, animal welfare organizations and pest controllers need to be made aware of the possible introduction of Rhipicephalus spp. to ensure early recognition and rapid elimination of the ticks.