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Target leaf spot disease (TLS), caused by Corynespora cassiicola (Berk & Curt) Wei, ranks among the most serious fungal diseases affecting cucumber production. However, the genetic basis for TLS resistance in cucumber has not yet been determined. In this study, we evaluated TLS resistance in the adult plants of 130 cucumber accessions using a disease index (DI) in October 2021, June 2023, and October 2023. The accessions used in this study were representative collection selected from the global 3,342 accessions, and contain four ecotypes (the Eurasian, Indian, East Asian, and Xishuangbanna type). Cluster analysis suggested that 11 of the 130 accessions exhibited high levels of TLS resistance (CG28, CG70, CG84, CG86, CG100, CG104, CG98, R163, R61, CG64, CG71). A genome-wide association study (GWAS) analysis was then performed based on the BLUP value of the DI collected from these three seasons, and three loci ( gTLS5.1 , gTLS5.2 , and gTLS7.1 ) associated with TLS on two chromosomes were identified. Seven candidate genes linked to disease resistance and abiotic stress were identified through functional annotation with Arabidopsis orthologous genes and pairwise linkage disequilibrium (LD) correlation analysis. Sequence alignment, expression and haplotype analysis further indicated to five of these candidate genes as being potentially causal to TLS: CsaV3_5G010580 for gTLS5.1 , and CsaV3_7G026140, CsaV3_7G026180 , CsaV3_7G026200 and CsaV3_7G026220 for gTLS7.1 . These genes related to TLS resistance in cucumber, could be useful to promote cucumber breeding and development.

Cucumber target leaf spot, caused by Corynespora cassiicola, is an emerging disease with a high incidence that causes severe damage to cucumbers on a global scale. Therefore, efforts need to be undertaken to limit the spread and infection of this pathogen, preferably by using environmentally friendly methods. In this study, the effects of temperature and moisture on the sporulation of C. cassiicola were investigated in vitro and in vivo. The novelty of our study refers to the observation of spore production and size as well as the revelation of a correlation between spore size and virulence. On potato dextrose agar (PDA) and cucumber−leaf extract agar (CEA), temperature played a critical role in spore production, which was strongly influenced by both temperature and moisture on detached leaves and cucumber seedlings. Maximum spore production was found at 30 °C on PDA and 25 °C on CEA, cucumber detached leaves and living plants. Lower spore productions were observed with a stepwise change of 5 °C. In addition, the largest spore production was found at 100% relative humidity (RH) in comparison to the other tested moisture. Moreover, moisture was found to be the most important factor affecting spore size, accounting for 83.09–84.86% of the total variance in length and 44.72–73.10% of the total variance in width. The longest−narrowest spores were formed at 100% RH, and the shortest−widest spores were formed at 75% RH. Furthermore, the result showed that larger spores of C. cassiicola were more virulent and small spores were avirulent. Our findings will contribute to the development of new strategies for the effective alleviation and control of cucumber target leaf spot.

The occurrence of diseases during greenhouse vegetable cultivation is becoming increasingly severe. Humidity and wind are important factors affecting the spread of many pathogenic fungal spores, but it remains difficult to explain the phenomenon of rapid spore spread in greenhouses. Here, the detachment of spores from hyphae during rapid drops in humidity and their subsequent dispersal due to wind is detailed. It is demonstrated that Corynespora cassiicola spores exhibit jerking movements during humidity reduction, resulting in spore discharge, and that spore connections are weaker in high‐humidity environments than in low‐humidity environments. This investigation across the fungal kingdom further reveals that jerking movements are common in the tested hyphomycete spore species. Spores rely mainly on wind to spread after being discharged from hyphae, and their spread range is influenced by factors such as wind speed, spore source height, and spore age. In summary, it is discovered that the combined effects of diurnal humidity fluctuations and wind drive the rapid spread of pathogenic spores in greenhouses, providing a theoretical basis for optimizing control strategies for airborne fungal diseases in greenhouses.

In this study, we compared differences between cucumber cultivars D9320 (highly resistant) and D0401 (highly susceptible) after Corynespora cassiicola infection. There were no differences in conidial germination or penetration between these cultivars. The germ tube rapidly elongated within 24 h after inoculation and invaded the cucumber leaf tissue directly or via stomata 24 h after inoculation (hai). The hypersensitive response (HR) was observed using in vitro inoculation and the trypan blue staining method. Necrotic spots occurred only in the resistant cucumber cultivar D9320 in vitro inoculation. The water-soaked lesion appeared in D0401. The HR was observed earlier and stronger in D9320 than in D0401 in the light microscope. The phenomenon of hypersensitive cell death could be earlier observed by using light microscope. The accumulation of H 2 O 2 , lignin and callose was observed in D9320 and D0401 infected with C. cassiicola . The order of accumulation was H 2 O 2 , lignin, and then callose. H 2 O 2 , lignin and callose accumulation in D9320 occurred earlier and stronger than in D0401. The pattern of callose accumulation in D0401 was different from that of H 2 O 2 and lignin. The intensity of the accumulation did not change over time in D0401. Consistent with lignin and callose accumulation, the expression levels of CsCALS and CsCCoAMT - increased earlier and were higher in the highly resistant D9320 cultivar than in the highly susceptible D0401 cultivar. The HR occurred after these accumulations. The amount of mycelium in D9320 was much lower than that in D0401 at 5 days post-infection (dpi). The HR plays a role in resisting infection of C. cassiicola .

The target leaf spot, an end-of-cycle disease that can cause soybean losses of 40%, is caused by the fungus Corynespora cassiicola. The aim of this study was to devise and validate a multiplex PCR protocol for the molecular identification of C. cassiicola, in both pure cultures of isolates and plants. For the analysis, a total of 75 samples was evaluated been 57 possible fungal isolates of C. cassiicola and 18 soybean plants displaying symptoms associated with target leaf spot. Total DNA was extracted from the pure cultures of the isolates and of four symptomatic leaf discs taken from each of the plants soybean evaluated and submitted to the conventional multiplex PCR technique using two primer pairs: ITS-1/ITS-4 and GA4-F/GA4-R. The ideal conditions for multiplex PCR were adjusted. The 57 isolates identified as C. cassiicola and among the evaluated plants, 15 samples were infected. Conventional multiplex PCR is efficient at identifying Corynespora cassiicola isolates and for rapid diagnosis of infected plants.

Thanatephorus cucumeris is a ubiquitous fungus responsible for many types of plant diseases worldwide. All isolates from infected Hevea brasiliensis trees secreted pectolytic enzymes; polygalacturonase (PG), pectin lyase (PL) and cellulolytic enzymes; beta-glucosidase and cellobiase in culture. The extracts of the rubber tree leaf tissues, inoculated with T. cucumeris did not show any PG activity. However, PL activity was detected in tissue with the establishment of the infection. The levels of beta-glucosidase, an inherent enzyme in Hevea spp. increased rapidly following infection. However, cellobiase was detected only with the initiation of infection. Molecular weights of PG in all isolates were similar and in the range of 53,000 to 58,000. PL also followed the same pattern showing a molecular weight around 39,000.

Tomato crops are susceptible to multiple diseases, several of which may be present during the same season. Therefore, rapid disease identification could enhance crop management consequently increasing the yield. In this study, nondestructive methods were developed to detect diseases that affect tomato crops, such as bacterial spot (BS), target spot (TS), and tomato yellow leaf curl (TYLC) for two varieties of tomato (susceptible and tolerant to TYLC only) by using hyperspectral sensing in two conditions: a) laboratory (benchtop scanning), and b) in field using an unmanned aerial vehicle (UAV-based). The stepwise discriminant analysis (STDA) and the radial basis function were applied to classify the infected plants and distinguish them from noninfected or healthy (H) plants. Multiple vegetation indices (VIs) and the M statistic method were utilized to distinguish and classify the diseased plants. In general, the classification results between healthy and diseased plants were highly accurate for all diseases; for instance, when comparing H vs. BS, TS, and TYLC in the asymptomatic stage and laboratory conditions, the classification rates were 94%, 95%, and 100%, respectively. Similarly, in the symptomatic stage, the classification rates between healthy and infected plants were 98% for BS, and 99–100% for TS and TYLC diseases. The classification results in the field conditions also showed high values of 98%, 96%, and 100%, for BS, TS, and TYLC, respectively. The VIs that could best identify these diseases were the renormalized difference vegetation index (RDVI), and the modified triangular vegetation index 1 (MTVI 1) in both laboratory and field. The results were promising and suggest the possibility to identify these diseases using remote sensing.

Soybean is one of the world’s major oil-bearing crops and occupies an important role in the daily diet of human beings. However, the frequent occurrence of soybean leaf diseases caused serious threats to its yield and quality during soybean cultivation. Rapid identification of soybean leaf diseases could provide a better solution for efficient control and subsequent precision application. In this study, a lightweight deep convolutional neural network (CNN) based on multiscale feature extraction fusion (MFEF) and combined with a dense connectivity (DC) network (MFEF-DCNet) was proposed for soybean leaf disease identification. In MFEF-DCNet, a multiscale feature extraction fusion (MFEF) module for soybean leaves was constructed by utilizing a convolutional attention module and depth-separable convolution to improve the model feature extraction capability. Multiscale features are fused by using dense connections (DC) in the backbone network to improve the model generalization capability. Experiments were implemented on eight distinct disease and deficiency classes of soybean images (including bacterial blight, cercospora leaf blight, downy mildew, frogeye leaf spot, healthy, potassium deficiency, soybean rust, and target spot) using the proposed network. The results showed that the MFEF-DCNet had an accuracy of 0.9470, an average precision of 0.9510, an average recall of 0.9480, and an F1-score of 0.9490 for soybean leaf disease identification. And MFEF-DCNet had certain performance advantages in terms of classification accuracy, convergence speed and other effects compared with VGG16, ResNet50, DenseNet201, EfficientNetB0, Xception and MobileNetV3_small models. In addition, the accuracy of the MFEF-DCNet model in recognizing soybean diseases in local data was 0.9024, which indicated that the MFEF-DCNet model had favorable application in practical applications. The proposed model and experience in this study could provide useful inspiration for automated disease identification in soybean and other crops.

Every nation’s development depends on agriculture. The term “cash crops” refers to cotton and other important crops. Most pathogens that significantly harm crops also impact cotton. Numerous diseases that influence yield via the leaf, such as powdery mildew, leaf curl, leaf spot, target spot, bacterial blight, and nutrient deficiencies, can affect cotton. Early disease detection protects crops from additional harm. Computerized methods perform a vital role in cotton leaf disease detection at an early stage. The method consists of two core steps such as feature extraction and classification. First, in the proposed method, data augmentation is applied to balance the input data. After that, features are extracted from a pre-trained VGG-16 model and passed to 11 fully convolutional layers, which freeze the majority and randomly initialize convolutional features to subsequently generate a score of the anomaly map, which defines the probability of the lesion region. The proposed model is trained on the selected hyperparameters that produce great classification results. The proposed model performance is evaluated on two publicly available Kaggle datasets, Cotton Leaf and Disease. The proposed method provides 99.99% accuracy, which is competent compared to existing methods.

Apple trees face various challenges during cultivation. Apple leaves, as the key part of the apple tree for photosynthesis, occupy most of the area of the tree. Diseases of the leaves can hinder the healthy growth of trees and cause huge economic losses to fruit growers. The prerequisite for precise control of apple leaf diseases is the timely and accurate detection of different diseases on apple leaves. Traditional methods relying on manual detection have problems such as limited accuracy and slow speed. In this study, both the attention mechanism and the module containing the transformer encoder were innovatively introduced into YOLOV5, resulting in YOLOV5-CBAM-C3TR for apple leaf disease detection. The datasets used in this experiment were uniformly RGB images. To better evaluate the effectiveness of YOLOV5-CBAM-C3TR, the model was compared with different target detection models such as SSD, YOLOV3, YOLOV4, and YOLOV5. The results showed that YOLOV5-CBAM-C3TR achieved mAP@0.5, precision, and recall of 73.4%, 70.9%, and 69.5% for three apple leaf diseases including Alternaria blotch, Grey spot, and Rust. Compared with the original model YOLOV5, the mAP 0.5increased by 8.25% with a small change in the number of parameters. In addition, YOLOV5-CBAM-C3TR can achieve an average accuracy of 92.4% in detecting 208 randomly selected apple leaf disease samples. Notably, YOLOV5-CBAM-C3TR achieved 93.1% and 89.6% accuracy in detecting two very similar diseases including Alternaria Blotch and Grey Spot, respectively. The YOLOV5-CBAM-C3TR model proposed in this paper has been applied to the detection of apple leaf diseases for the first time, and also showed strong recognition ability in identifying similar diseases, which is expected to promote the further development of disease detection technology.