Search Results (43)

Citrus diseases can lead to surface defects on citrus fruits, adversely affecting their quality. This study aims to accurately identify citrus defects against varying backgrounds by focusing on four types of diseases: citrus black spot, citrus canker, citrus greening, and citrus melanose. We propose an improved YOLOv10-based disease detection method that replaces the traditional convolutional layers in the Backbone network with LDConv to enhance feature extraction capabilities. Additionally, we introduce the GFPN module to strengthen multi-scale information interaction through cross-scale feature fusion, thereby improving detection accuracy for small-target diseases. The incorporation of the DAT mechanism is designed to achieve higher efficiency and accuracy in handling complex visual tasks. Furthermore, we integrate the AFPN module to enhance the model’s detection capability for targets of varying scales. Lastly, we employ the Slide Loss function to adaptively adjust sample weights, focusing on hard-to-detect samples such as blurred features and subtle lesions in citrus disease images, effectively alleviating issues related to sample imbalance. The experimental results indicate that the enhanced model YOLOv10-LGDA achieves impressive performance metrics in citrus disease detection, with accuracy, recall, mAP@50, and mAP@50:95 rates of 98.7%, 95.9%, 97.7%, and 94%, respectively. These results represent improvements of 4.2%, 3.8%, 4.5%, and 2.4% compared to the original YOLOv10 model. Furthermore, when compared to various other object detection algorithms, YOLOv10-LGDA demonstrates superior recognition accuracy, facilitating precise identification of citrus diseases. This advancement provides substantial technical support for enhancing the quality of citrus fruit and ensuring the sustainable development of the industry. Full article

Lemon, widely used in food, medicine, cosmetics, and other industries, has considerable value as a commodity and horticultural product. Previous research has shown that the fungus Diaporthe citri infects several citrus species, including mandarin, lemon, sweet orange, pomelo, and grapefruit, in China. Although D. citri has been reported to cause melanose disease in lemons in China, key pathological evidence, such as Koch’s postulates fulfillment on lemon fruits and detailed morphological characterization, is still lacking. In May 2018, fruits, leaves, and twigs were observed to be infected with melanose disease in lemon orchards in Chongqing municipality in China. The symptoms appeared as small black discrete spots on the surface of fruits, leaves, and twigs without obvious prominent and convex pustules. D. citri was isolated consistently from symptomatic organs and identified provisionally based on the morphological characteristics. The identification was confirmed using sequencing and multigene phylogenetic analysis of ITS, TUB, TEF, HIS, and CAL regions. Pathogenicity tests were performed using a conidium suspension, and melanose symptoms similar to those observed in the field were reproduced. To our knowledge, this study provides the first comprehensive evidence for D. citri as a causal agent of melanose disease in lemons in China, including morphological characterization and pathogenicity assays on lemon fruits. This report broadens the spectrum of hosts of D. citri in China and provides useful information for the management of melanose in lemons. Full article

The genus Alternaria, functioning as a saprobe, endophyte, and plant pathogen, is widely distributed across various natural and human-impacted environments. Leaf spot and black spot diseases, caused by Alternaria species, are the most prevalent plant diseases within this genus, leading to significant reductions in crop yields and substantial economic losses. To facilitate the timely detection of Alternaria species during the early stages of infection, enable targeted treatments, and mitigate associated damages, we employed a species identification method based on Analysis of whole-GEnome (AGE). We downloaded 148 genomes, including 31 Alternaria species, from the NCBI GenBank database. Through bioinformatics analysis, we constructed a specific-target sequence library and selected a representative sequence per species. The specific target sequences of the seven exemplary Alternaria species were subsequently used for validation and rapid detection, utilizing Sanger sequencing and CRISPR-Cas12a technology, respectively. The results demonstrated that our method accurately identified the target species. Additionally, by combining Enzymatic Recombinase Amplification (ERA) with CRISPR-Cas12a, we achieved rapid and precise identification of genomic DNA samples, with a detection limit as low as 0.01 ng/µL within 30 min. Therefore, AGE proves to be a highly robust and efficient method for the detection of Alternaria species, offering broad potential for various applications. Full article

This study explores the external morphology of larva of Cricula trifenestrata Helfer at the fifth instar stage, focusing on sexual dimorphism, scoli, and fluorescence hair warts. The larva displays a black body adorned with varying shades of orange to crimson–red transverse stripes and small yellow dorsal spots. Longitudinal stripes with fluorescent warts are observed in the subspiracular region, accompanied by an overall coverage of long white hairs. These distinctive features, including scoli and fluorescence hair warts, serve as effective defense mechanisms against predators and parasitoids. The results enhance our understanding of C. trifenestrata Helfer larval biology, providing valuable insights for entomology and evolutionary biology. The identification of species-specific adaptations, particularly the presence of scoli and fluorescence hair warts, underscores their significance in shaping survival strategies and ecological interactions. Full article

Strawberries hold significant economic importance in both German and global agriculture. However, their yield is often adversely affected by fungal diseases. This study describes Neopestalotiopsis rosae as a newly emerging pathogen responsible for leaf blight and fruit rot in strawberries in Germany. Infected plants were observed in Hohenheim, Germany. A combination of morphological and molecular analyses, along with pathogenicity tests, confirmed the identity of N. rosae as the causal agent. Morphological examination of conidia and mycelium revealed key characteristics including the presence of versicolorous median cells, conidial appendages, black spherical conidiomata formation as well as changing colony color and fluffy texture. These properties align with the established descriptions for the species. Molecular analysis, particularly the sequencing of the internal transcribed spacer and β-tubulin regions allowed the precise identification of the pathogen. Artificial inoculation of healthy strawberry plants with conidial suspension derived from the isolated strain resulted in the development of characteristic symptoms, including necrotic leaf spots and water-soaked fruit lesions, similar to those observed on the original infected plants. To our knowledge, this study presents the first documented occurrence of N. rosae in Germany, highlighting its emergence as a significant threat to strawberry production in Europe. Full article

Ginkgo biloba is a multifunctional composite tree species that has important ornamental, economic, medicinal, and scientific research value. In October 2023, the foliage of G. biloba on the campus of Nanjing Forestry University exhibited leaf blight. Black-brown necrotic spots were observed on a large number of leaves, with a disease incidence of 86%. After isolating a fungus from symptomatic leaves, pathogenicity was tested to satisfy Koch’s postulates. Using morphological features and multi-gene phylogenetic analyses of an internal transcribed spacer (ITS), elongation factor 1-alpha (EF1-α), and beta-tubulin (β-tub), the isolates YKB1-1 and YKB1-2 were identified as Nigrospora oryzae. N. oryzae was previously reported as an endophyte of G. biloba. However, this study shows it to be pathogenic to G. biloba, causing leaf spots. Two endophytic bacteria were isolated from asymptomatic leaves of diseased G. biloba trees, and their molecular identification was performed using 16S ribosomal DNA (16S rDNA). GBB1-2 was identified as Bacillus altitudinis, while GBB1-5 was identified as Bacillus amyloliquefaciens. The screening and verification of endophytic bacteria provide a new strategy for the control of N. oryzae. Full article

Pear black spot, caused by A. gaisen during fruit growth, is a disease that significantly reduces pear yield. Biological control using antagonistic microorganisms is regarded as a viable alternative to chemical agents. The discovery of TRM76147, a novel species of Streptomyces isolated from the Taklamakan Desert, has demonstrated promising potential in addressing this issue. This study was conducted to determine the potential of crude extract of Streptomyces sp. nov., strain TRM76147, for control of A. gaisen. TRM76147 is closely related to Streptomyces griseoviridis NBRC 12874^T, exhibiting an average nucleotide identity (ANI) value of 82.13%. Combined with the polyphasic taxonomic identification, this suggests that TRM76147 is a potentially new species. Through analyses using BigSCAPE and antiSMASH, it was determined that the TRM76147 genome contains 19 gene clusters. The ethyl acetate extract of this strain demonstrates antifungal activity, with the active substance remaining stable at temperatures up to 70 °C, achieving an activity level of 16.23 ± 0.22 mm. Furthermore, the crude extract maintains its antifungal efficacy across a pH range of 2 to 12. Notably, the antifungal diameter was recorded at 16.53 ± 0.12 mm following 80 min of UV irradiation. Under different treatment conditions, TRM76147 fermentation crude extract caused A. gaisen spore crumpling and spore number reduction. In addition, this study also found that the TRM76147 fermentation broth could control the production of pear black spot disease, which initially revealed the inhibition mechanism. The abundant actinomycete resources in this study have good application and development value in the discovery of new species and the study of bioactive substances and biological control. Full article

Postharvest avocado is susceptible to attack by Neofusicoccum parvum, which has been reported to cause black spot in avocado pulp. Therefore, it is necessary to look for alternatives for its control with products that are low-cost, effective and without risks to human health and the environment, and that also stimulate the defense mechanisms of the fruit. The aim was to evaluate the effect of basic and GRAS substance treatments on the in vitro control of N. parvum, and the induction of enzymes related to the defense mechanisms of the fruit. N. parvum was isolated from avocado fruit. Morphological and molecular identification was performed. In vitro and in vivo treatments were made for the control of pathogens and the induction of defense mechanisms in the fruit with basic and GRAS substance treatments based on chitosan and GRAS substances. The basic and GRAS substance treatments inhibited the development of N. parvum mycelium by 80–100%. In addition, they induced the activation of enzymes related to the defense mechanisms of the fruit (PAL, POD and PPO). The best basic and GRAS substance treatments, both in vitro and in vivo, were those based on chitosan (0.5%) and cinnamon essential oil (2.5%) when they were applied combined in the form of an emulsion. Full article

Black spot disease, caused by Alternaria alternata, results in enormous losses in broccoli production. The current measures to prevent black spot disease mainly rely on seed disinfection and chemical control, but excellent disease-resistance resources are relatively scarce. In this study, we screened primers for black spot disease identification and conducted black spot disease resistance identification of 173 lines, including 70 hybrid lines and 103 inbred lines. Based on the phenotype, we have set five grades to present different symptoms of illness: high disease resistance, disease resistance, disease tolerance, susceptibility, and high susceptibility (the disease resistance gradually weakens). According to our phenotypic evaluations, 3, 55, 65, 45, and 5 lines were classified into high disease resistance, disease resistance, disease tolerance, susceptible, and high susceptibility, respectively. By comparing the proportion of resistant lines between hybrid and inbred lines, we noticed that the frequency of hybrid varieties with high disease resistance and disease resistance (28.57%) was lower than that in inbred lines (36.89%), indicating that the resistance resources have not yet been effectively utilized in hybrid broccoli breeding. Therefore, our results identified the resistance resources to black spot disease in broccoli, which lays the foundation for the exploration of disease resistance genes as well as the analysis of disease resistance mechanisms in the future. Full article

Traditionally, research in the field of traffic safety has predominantly focused on two key areas—the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often suffer from missing or incomplete information. Moreover, these records typically offer limited insight into the various attributes associated with accidents, thereby posing challenges to comprehensive analyses. Furthermore, the collection and management of such data incur substantial costs. Consequently, there is a pressing need to explore how the features of the urban built environment can effectively facilitate the accurate identification and analysis of traffic black spots, enabling the formulation of effective management strategies to support urban development. In this study, we research the Kowloon Peninsula in Hong Kong, with a specific focus on road intersections as the fundamental unit of our analysis. We propose leveraging street view images as a valuable source of data, enabling us to depict the urban built environment comprehensively. Through the utilization of models such as random forest approaches, we conduct research on traffic black spot identification, attaining an impressive accuracy rate of 87%. To account for the impact of the built environment surrounding adjacent road intersections on traffic black spot identification outcomes, we adopt a node-based approach, treating road intersections as nodes and establishing spatial relationships between them as edges. The features characterizing the built environment at these road intersections serve as node attributes, facilitating the construction of a graph structure representation. By employing a graph-based convolutional neural network, we enhance the traffic black spot identification methodology, resulting in an improved accuracy rate of 90%. Furthermore, based on the distinctive attributes of the urban built environment, we analyze the underlying causes of traffic black spots. Our findings highlight the significant influence of buildings, sky conditions, green spaces, and billboards on the formation of traffic black spots. Remarkably, we observe a clear negative correlation between buildings, sky conditions, and green spaces, while billboards and human presence exhibit a distinct positive correlation. Full article

Rice production in the Anhui province is threatened by fungal diseases. We obtained twenty-five fungal isolates from rice and wild rice leaves showing leaf spot disease collected along the Yangtze River. A phylogenetic analysis based on internal transcribed spacer (ITS), translation elongation factor 1 alpha (TEF1-α), and beta tubulin (TUB2) sequences revealed one isolate (SS-2-JB-1B) grouped with Nigrospora sphaerica, one (QY) with Nigrospora chinensis, twenty-two with Nigrospora oryzae, and one isolate (QY-2) grouped in its own clade, which are related to but clearly different from N. oryzae. Nineteen tested isolates, including sixteen strains from the N. oryzae clade and the three isolates of the other three clades, caused disease on detached rice leaves. The three isolates that did not belong to N. oryzae were also able to cause disease in rice seedlings, suggesting that they were rice pathogens. Isolate QY-2 differed from the other isolates in terms of colony morphology, cell size, and susceptibility to fungicides, indicating that this isolate represents a new species that we named Nigrospora anhuiensis. Our analysis showed that N. sphaerica, N. chinensis, and the new species, N. anhuiensis, can cause rice leaf spot disease in the field. This research provides new knowledge for understanding rice leaf spot disease. Full article

Identifying accidents in road black spots is crucial for improving road safety. Traditional methodologies, although insightful, often struggle with the complexities of imbalanced datasets, while machine learning (ML) techniques have shown promise, our previous work revealed that supervised learning (SL) methods face challenges in effectively distinguishing accidents that occur in black spots from those that do not. This paper introduces a novel approach that leverages positive-unlabeled (PU) learning, a technique we previously applied successfully in the domain of defect detection. The results of this work demonstrate a statistically significant improvement in key performance metrics, including accuracy, precision, recall, F1-score, and AUC, compared to SL methods. This study thus establishes PU learning as a more effective and robust approach for accident classification in black spots, particularly in scenarios with highly imbalanced datasets. Full article

The cultivation of kimchi cabbage in South Korea has always faced significant challenges due to the looming presence of Alternaria leaf spot (ALS), which is a fungal disease mainly caused by Alternaria alternata. The emergence of black spots resulting from Alternaria infection lowers the quality of the plant, rendering it inedible and unmarketable. The timely identification of this disease is crucial, as it provides essential data enabling swift intervention, thereby localizing the infection throughout the field. Hyperspectral imaging technologies excel in detecting subtle shifts in reflectance values induced by chemical differences within leaf tissues. However, research on the spectral correlation between Alternaria and kimchi cabbage remains relatively scarce. Therefore, this study aims to identify the spectral signature of Alternaria infection on kimchi cabbage and develop an automatic classifier for detecting Alternaria disease symptoms. Alternaria alternata was inoculated on various sizes of kimchi cabbage leaves and observed daily using a hyperspectral imaging system. Datasets were created based on captured hyperspectral images to train four classifier models, including support vector machine (SVM), random forest (RF), one-dimensional convolutional neural network (1D-CNN), and one-dimensional residual network (1D-ResNet). The results suggest that 1D-ResNet outperforms the other models with an overall accuracy of 0.91, whereas SVM, RF, and 1D-CNN achieved 0.80, 0.88, and 0.86, respectively. This study may lay the foundation for future research on high-throughput disease detection, frequently incorporating drones and aerial imagery. Full article

Rhizoctonia solani is a significant pathogen affecting various crops, including tobacco. In this study, a bacterial strain, namely Y246, was isolated from the soil of healthy plants and exhibited high antifungal activity. Based on morphological identification and DNA sequencing, this bacterial strain was identified as Bacillus safensis. The aim of this investigation was to explore the antifungal potential of strain Y246, to test the antifungal stability of Y246 by adjusting different cultivation conditions, and to utilize gas chromatography–mass spectrometry (GC-MS) to predict the volatile compounds related to antifungal activity in Y246. In vitro assays demonstrated that strain Y246 exhibited a high fungal inhibition rate of 76.3%. The fermentation broth and suspension of strain Y246 inhibited the mycelial growth of R. solani by 66.59% and 63.75%, respectively. Interestingly, treatment with volatile compounds derived from the fermentation broth of strain Y246 resulted in abnormal mycelial growth of R. solani. Scanning electron microscopy analysis revealed bent and deformed mycelium structures with a rough surface. Furthermore, the stability of antifungal activity of the fermentation broth of strain Y246 was assessed. Changes in temperature, pH value, and UV irradiation time had minimal impact on the antifungal activity, indicating the stability of the antifungal activity of strain Y246. A GC-MS analysis of the volatile organic compounds (VOCs) produced by strain Y246 identified a total of 34 compounds with inhibitory effects against different fungi. Notably, the strain demonstrated broad-spectrum activity, exhibiting varying degrees of inhibition against seven pathogens (Alternaria alternata, Phomopsis. sp., Gloeosporium musarum, Dwiroopa punicae, Colletotrichum karstii, Botryosphaeria auasmontanum, and Botrytis cinerea). In our extensive experiments, strain Y246 not only exhibited strong inhibition against R. solani but also demonstrated remarkable inhibitory effects on A. alternata-induced tobacco brown spot and kiwifruit black spot, with impressive inhibition rates of 62.96% and 46.23%, respectively. Overall, these findings highlight the significant antifungal activity of B. safensis Y246 against R. solani. In addition, Y246 has an excellent antifungal stability, with an inhibition rate > 30% under different treatments (temperature, pH, UV). The results showed that the VOCs of strain Y246 had a strong inhibitory effect on the colony growth of R. solani, and the volatile substances produced by strain Y246 had an inhibitory effect on R. solani at rate of 70.19%. Based on these results, we can conclude that Y246 inhibits the normal growth of R. solani. These findings can provide valuable insights for developing sustainable agricultural strategies. Full article

Sustainable transportation goals include an improvement in the level of road safety worldwide. It is well known that traffic accidents are one of the major causes of death worldwide. Black spots are road locations with a higher than statistically expected number of accidents. Remedying black spots would decisively improve road safety. A literature review of black spot identification methods, i.e., accident numbers, accident rates related to exposure, severity of accidents, Poisson and quality control methods, is presented within the framework of this paper. The various approaches adopted by key European and other countries are also summarized and evaluated. An important parameter is the unit length of a road, where accidents are referred. The quality of accident records is also critical. It is concluded that the coupling of statistical and accident severity index methods can contribute to assessing road infrastructure in a more holistic way and, therefore, in providing more reliable results with regard to the road safety level. The design and implementation of effective road safety strategies, based on black spot analysis, can be of great value for the decision makers and decision takers who are involved in the development of a sustainable transportation system. Full article