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Bacillus spp. endospores are important dormant cell forms and are distributed widely in environmental samples. While these endospores can have important industrial value (e.g. use in animal feed as probiotics), they can also be pathogenic for humans and animals, emphasizing the need for effective endospore detection. Standard spore detection by colony forming units (CFU) is time‐consuming, elaborate and prone to error. Manual spore detection by spore count in cell counting chambers via phase‐contrast microscopy is less time‐consuming. However, it requires a trained person to conduct. Thus, the development of a facilitated spore detection tool is necessary. This work presents two alternative quantification methods: first, a colorimetric assay for detecting the biomarker dipicolinic acid (DPA) adapted to modern needs and applied for Bacillus spp. and second, a model‐based automated spore detection algorithm for spore count in phase‐contrast microscopic pictures. This automated spore count tool advances manual spore detection in cell counting chambers, and does not require human overview after sample preparation. In conclusion, this developed model detected various Bacillus spp. endospores with a correctness of 85–89%, and allows an automation and time‐saving of Bacillus endospore detection. In the laboratory routine, endospore detection and counting was achieved within 5–10 min, compared to up to 48 h with conventional methods. The DPA‐assay on the other hand enabled very accurate spore detection by simple colorimetric measurement and can thus be applied as a reference method.

The Centers for Disease Control and Prevention (CDC) classifies Bacillus anthracis as one of the most dangerous pathogens that may affect public health and national security. Due to its importance as a potential biological weapon, this bacteria has been classified in the highest category A, together with such pathogens as variola virus or botulinum neurotoxin. Characteristic features of this pathogen that increase its military importance are the ease of its cultivation, transport, and storage and its ability to create survival forms that are extremely resistant to environmental conditions. However, beyond bioterrorism, B. anthracis is also a naturally occurring pathogen. Anthrax outbreaks occur in livestock and wildlife, particularly in spore-contaminated regions of Africa, Asia, and North America. Spores persist for decades, leading to recurrent infections and zoonotic transmission through direct contact, inhalation, or consumption of contaminated meat. This work presents a new electrochemical method for detecting and quantifying B. anthracis in spore form using a selective immune reaction. The developed method is based on the thiol-modified electrodes that constitute the sensing element of the electrochemical system. Tests with the B. anthracis spore suspension showed that the detection limit for this pathogen is as low as 103 CFU/mL. Furthermore, it was possible to quantify the analyte with a sensitivity of 11 mV/log (CFU/mL). Due to several features, such as low unit cost, portability, and minimal apparatus demands, this method can be easily implemented in field analyzers for this pathogen and provides an alternative to currently used techniques and devices.

Fungal diseases are the main factors affecting the quality and production of vegetables. Rapid and accurate detection of pathogenic spores is of great practical significance for early prediction and prevention of diseases. However, there are some problems with microscopic images collected in the natural environment, such as complex backgrounds, more disturbing materials, small size of spores, and various forms. Therefore, this study proposed an improved detection method of GCS-YOLOv8 (Global context and CARFAE and Small detector-optimized YOLOv8), effectively improving the detection accuracy of small-target pathogen spores in natural scenes. Firstly, by adding a small target detection layer in the network, the network’s sensitivity to small targets is enhanced, and the problem of low detection accuracy of the small target is effectively improved. Secondly, Global Context attention is introduced in Backbone to optimize the CSPDarknet53 to 2-Stage FPN (C2F) module and model global context information. At the same time, the feature up-sampling module Content-Aware Reassembly of Features (CARAFE) was introduced into Neck to enhance the ability of the network to extract spore features in natural scenes further. Finally, we used an Explainable Artificial Intelligence (XAI) approach to interpret the model’s predictions. The experimental results showed that the improved GCS-YOLOv8 model could detect the spores of the three fungi with an accuracy of 0.926 and a model size of 22.8 MB, which was significantly superior to the existing model and showed good robustness under different brightness conditions. The test on the microscopic images of the infection structure of cucumber down mildew also proved that the model had good generalization. Therefore, this study realized the accurate detection of pathogen spores in natural scenes and provided feasible technical support for early predicting and preventing fungal diseases.

The severity of rice blast and its impacts on rice yield are closely related to the inoculum quantity of Magnaporthe oryzae, and automatic detection of the pathogen spores in microscopic images can provide a rapid and effective way to quantify pathogen inoculum. Traditional spore detection methods mostly rely on manual feature extraction and shallow machine learning models, and are mostly designed for the indoor counting of a single spore class, which cannot handle the interference of impurity particles in the field. This study achieved automatic detection of rice blast fungus spores in the mixture with other fungal spores and rice pollens commonly encountered under field conditions by using deep learning based object detection techniques. First, 8959 microscopic images of a single spore class and 1450 microscopic images of mixed spore classes, including the rice blast fungus spores and four common impurity particles, were collected and labelled to form the benchmark dataset. Then, Faster R-CNN, Cascade R-CNN and YOLOv3 were used as the main detection frameworks, and multiple convolutional neural networks were used as the backbone networks in training of nine object detection algorithms. The results showed that the detection performance of YOLOv3_DarkNet53 is superior to the other eight algorithms, and achieved 98.0% mean average precision (intersection over union > 0.5) and an average speed of 36.4 frames per second. This study demonstrated the enormous application potential of deep object detection algorithms in automatic detection and quantification of rice blast fungus spores.

Rice blast is an important fungal disease caused by Magnaporthe oryzae , and the air-borne disease can erupt in a short time, causing large-scale yield losses. Rapid and accurate detection of rice blast spores in microscopic images is crucial for monitoring spore density in the field, guiding farmers in timely pesticide application for effective prevention and control. However, there is a lack of an efficient and accurate method for detecting rice blast spores under complex field conditions currently. Traditional machine learning algorithms are better suited for detecting a single category under controlled conditions and rely on manual feature extraction, which limits their transferability. Therefore, this paper proposed a novel Rice Blast Spore Detector based on You Only Look Once algorithm - YOLO-RBSD, which can effectively detect rice blast spores and three categories of impurity particles in microscopic images against complex field backgrounds. Firstly, the detector introduced the triplet attention mechanism on the basis of YOLOv8s, enhancing its ability to capture cross-dimensional features. In addition, in order to further reduce model parameters and enhance model speed, the detector replaced partial CSPDarknet53 to 2-Stage FPN (C2f) modules with Depthwise Separable C2f (DSC2f) modules, realizing the optimal model structure design. Finally, YOLO-RBSD achieved a mean average precision (0.5) of 96.1% and a macro F1 score of 92.6%, processing 125 images per second, surpassing the mainstream models in both speed and accuracy. As an effective and lightweight tool, YOLO-RBSD provides a strong foundation for automated rice blast spore density monitoring in the field.

Stripe rust is one of the most common diseases challenging the safe production of wheat. Rapid identification and analysis of urediospores, responsible for disease transmission, is the key to preventing and controlling stripe rust. In this study, combined with chemical analysis, spectral analysis method and finite element simulation of the intrinsic characteristics of urediospore were studied in details. Firstly, a comparative analysis of the urediospore components was carried out by HPLC-MS, and a total of 31 components were extracted. On this basis, a 3D urediospore model was established by using FEM software, the characteristic frequencies and modes were calculated. The results shown that and the resonance frequencies and modes of the elliptical structure were lower and more diverse. The method and conclusion can lay a theoretical foundation for the accurate monitoring and early control of wheat stripe rust urediospores.

Asparagus stem blight is a devastating crop disease, and the early detection of its pathogenic spores is essential for effective disease control and prevention. However, spore detection is still hindered by complex backgrounds, small target sizes, and high annotation costs, which limit its practical application and widespread adoption. To address these issues, a semi-supervised spore detection framework is proposed for use under complex background conditions. Firstly, a difficulty perception scoring function is designed to quantify the detection difficulty of each image region. For regions with higher difficulty scores, a masking strategy is applied, while the remaining regions are adversarial augmentation is applied to encourage the model to learn from challenging areas more effectively. Secondly, a Gaussian Mixture Model is employed to dynamically adjust the allocation threshold for pseudo-labels, thereby reducing the influence of unreliable supervision signals and enhancing the stability of semi-supervised learning. Finally, the Wasserstein distance is introduced for object localization refinement, offering a more robust positioning approach. Experimental results demonstrate that the proposed framework achieves 88.9% mAP50 and 60.7% mAP50–95, surpassing the baseline method by 4.2% and 4.6%, respectively, using only 10% of labeled data. In comparison with other state-of-the-art semi-supervised detection models, the proposed method exhibits superior detection accuracy and robustness. In conclusion, the framework not only offers an efficient and reliable solution for plant pathogen spore detection but also provides strong algorithmic support for real-time spore detection and early disease warning systems, with significant engineering application potential.

This study addresses the challenge posed by the small spore size of tomato gray mold, which hinders its identification and enumeration by conventional techniques. This work presents a novel approach for quantifying spore counts of tomato gray mold using diffraction imaging technology and image processing techniques. To construct a device for acquiring diffraction images of tomato gray mold spores, initially, the hyperspectral data pertaining to the gray mold spores of tomatoes was obtained. The characteristic wavelength of the light source of the diffraction image acquisition device was obtained by smoothing, principal component analysis, and comprehensive coefficient weight calculation. Then, the key parameters of the system were simulated, and the diffraction image acquisition device was built. Finally, tomato gray mold spores were counted based on angular spectrum reconstruction and image processing. The findings indicated that the combined contribution rate of the initial and secondary principal components of the original spectral data obtained from tomato gray mold spore samples amounted to 92.271%. The visible range of 435 nm, 475 nm, and 720 nm can be selected as the light source for tomato gray molds spore diffraction imaging system. CMOS image sensor was installed 45 mm below the micropore with a diameter of 100 //m, and the diffraction image obtained by simulation has a clear diffraction fingerprint. The diffraction imaging system can collect diffraction images of disease spores, and the collected diffraction images have clear diffraction fingerprints. The experimental error range was 5.13%-8.57%, and the average error was 6.42%. The error was within a 95% consistency. Therefore, this study can provide a research basis for the classification and recognition of greenhouse disease spores.

Downy mildew of hops represents a serious disease affecting hops production in all growing regions. Disease management is primarily based on the application of fungicides at regular intervals based on a short-term forecasting methodology that is essential for evaluating the occurrence of theoretical infections. To enable a more reliable assessment of the pathogen’s presence in a given area, spore traps capturing airborne Pseudoperonospora humuli sporangia can be utilized. The use of quantitative real-time PCR (qRT-PCR) for the detection of sporangia collected by these traps allows for the elimination of laborious and time-consuming microscopic counting. Among four tested P. humuli-specific nuclear DNA sequences, an effective qRT-PCR detection method was developed based on the c127233.5e3 sequence. This detection approach was used for the quantification of sporangia from volumetric spore trap samples collected in situ under field conditions at three selected localities in Bohemia and Moravia during the 2021–2022 period. The obtained results were compared with the short-term forecasting method of the downy mildew (HDM) weather index (I) based on meteorological data. The overall course of the HDM weather index (I) closely correlated with the occurrence of sporangia: after reaching the maximum HDM weather index (I) value, the highest sporangium detection was observed with a time delay of 1–2 weeks at all the monitored sites. The results corresponded well with data obtained from volumetric spore traps in Germany, and the qRT-PCR method proved to be fully comparable to light microscopy. The combination of volumetric spore traps and qRT-PCR can significantly improve the precision of short-term forecasting systems for P. humuli infection, thereby enabling more efficient fungicide application programs in hops protection and contributing to a better understanding of the pathogen’s dispersal dynamics.