Journal of Phytopathology最新文献

The identification and control of potato leaf diseases pose considerable difficulties for worldwide agriculture, affecting both the quality and yield of crops. Addressing this issue, we investigate the efficacy of the lightweight YOLOv8 variants, namely YOLOv8n, YOLOv8s and YOLOv8m, for the automated detection and classification of different potato leaf states. These conditions are categorised into three types: healthy, early blight disease and late blight disease. Our findings show that YOLOv8n achieves a mean average precision (mAP) of 94.2%, YOLOv8s achieves a mAP of 93.4%, and YOLOv8m achieves a mAP of 94%. Building on these results, we propose a novel weighted ensembling technique based on the confidence score (WECS) to combine the predictions of these YOLOv8 variants. The WECS technique efficiently leverages the advantages of each YOLOv8 variant by assigning weights based on the confidence scores of individual model predictions. These weighted forecasts are then combined to produce a final ensemble prediction for each sample. Achieving 99.9% precision and 89.6% recall, the WECS method attains a global mean Average Precision (mAP) of 96.3%, showcasing its robustness in real-world applications.

Among soybean diseases, rust, caused by Phakopsora pachyrhizi, stands out as one of the most destructive. Resistance inducers may be a great alternative to reduce the yield losses caused by this disease from the perspective of more sustainable agriculture. In this study, soybean plants were sprayed with water (control) or with Cautha [referred to as induced resistance (IR) stimulus after that] and inoculated or non-inoculated with P. pachyrhizi. The germination of urediniospores was significantly reduced by 22%, 26%, 19%, and 25% for the IR stimulus rates of 2.5, 5, 10, and 20 mL/L, respectively. Rust severity was significantly reduced by 27%, 19%, 23%, 25%, and 41% at 7, 9, 11, 13, and 15 days after inoculation, respectively, and the area under the disease progress curve significantly decreased by 27% for IR stimulus-sprayed plants compared to water-sprayed plants. For infected plants, foliar concentrations of Ca, N, chlorophyll a + b, and carotenoids were higher for IR-stimulus sprayed plants than for water-sprayed plants. Lower concentrations of malondialdehyde (less cellular damage) and reactive oxygen species (hydrogen peroxide and superoxide anion radical) along with great activities of antioxidative enzymes (ascorbate peroxidase, catalase, glutathione reductase, and superoxide dismutase) helped to reduce rust symptoms for IR-stimulus sprayed plants. On top of that, these plants also showed greater foliar concentrations of total soluble phenols and lignin as well as increased activities of defence-related enzymes (chitinase, β-1,3-glucanase, phenylalanine ammonia-lyase, peroxidase, polyphenoloxidase, and lipoxygenase). These results strongly support the potential of using this IR stimulus to increase soybean resistance against infection by P. pachyrhizi and, at the same time, to act directly against the germination of the urediniospores.

Leaf spot and soft rot of ornamental plants were observed in the Alborz and Markazi provinces during the summer and autumn of 2022. Eighteen samples were collected from symptomatic ornamental plants in Karaj and Arak cities. Necrotic leaf spots surrounded by a broad yellow halo were observed in the affected pothos (Epipremnum aureum) and compact dragon (Dracaena compacta) plants. On the severely affected, leaves of compact dragon with a yellow margin were observed. Soft rot symptoms which turned dark brown areas were observed on the diseased jade plants (Crassula ovata). The colonies of bacterial strains were cream-coloured, round, convex, smooth and with entire margins on nutrient agar. In the pathogenicity of 35 recovered strains, the five strains isolated from pothos caused water-soaking 2 days after inoculation, which finally turned into necrotic lesions surrounded by chlorotic halos. Eight strains isolated from compact dragon produced necrotic spots on the inoculated plants. Five strains originated from jade plants induced soft rot 4–5 days post-inoculation. All 18 pathogenic strains were negative in Gram and oxidase reactions. The gyrB and infB gene sequences of eight representative strains were partially amplified and sequenced. In the maximum-likelihood phylogenetic tree created based on the concatenated sequences of gyrB and infB genes, the strains clustered with Enterobacter mori LMG 25706^T supported with bootstrap value of 100%. To the best of our knowledge, this is the first report of pothos and compact dragon leaf spot and jade plant soft rot caused by E. mori.

Salix babylonica L. is a popular ornamental and ecological tree species. In this study, the results of pathogenicity tests showed that the Botryosphaeria dothidea isolates JXL1-3, JXL1-5, JXL1-11, L1-3, L2-2, NFS1, NFS2, NFS5 and WLS1 were pathogens causing branch blight on S. babylonica. Meanwhile, the B. dothidea isolates JXL1-11, L1-3, NFS1 and WLS1 were found to be pathogenic to two other willows, S. chaenomeloides and S. suchowensis. The biological characteristics of B. dothidea isolates JXL1-11, L1-3, NFS1 and WLS1 were investigated under different conditions, including culture media, pH, temperatures, carbon/nitrogen sources and light. It was found that potato dextrose agar (PDA) medium was the most optimal medium for the B. dothidea isolates JXL1-11, L1-3, NFS1 and WLS1 mycelial growth, the suitable pH was 4, and the optimal temperatures were 25°C–30°C. The optimal carbon sources were glucose and sucrose, and the optimal nitrogen sources were ammonium sulfate and peptone. The optimal light condition for inducing sporulation was dark + ultraviolet light. This study provides a fundamental basis for the prevention and control the branch blight on willows and further studies.

Biological control by beneficial microorganisms is considered as one of the best alternatives to decrease the use of chemical fungicides. However, reliable and efficient solutions to manage crop diseases are still limited, and this is particularly true for seedborne fungal pathogens. As a first step towards identification of potential biological control agents (BCAs), a new methodological approach in planta was developed to control Alternaria brassicicola, a problematic fungal disease of Brassica crops. The method was used to evaluate the antagonistic activity of Trichoderma viridarium on transmission of the pathogen to seeds of Capsella bursa-pastoris (sheperd's purse). In vitro, inhibition of A. brassicicola and overgrowth by T. viridarium suggested mycoparasitism, which was confirmed by microscopical observations. In planta, siliques of C. bursa pastoris were co-inoculated with T. viridarium and A. brassicicola and seed transmission of each fungal species was evaluated. Pre-inoculation of T. viridarium 24 h before A. brassicicola significantly reduced the transmission of the pathogen to the seeds by almost 70%, compared with inoculation of A. brassicicola alone. The capacity of T. viridarium to reduce A. brassicicola seed transmission offers a promising alternative to conventional fungicides. However, in order to develop reliable biocontrol strategies, future research should focus on the underlying mechanisms involved in the interactions between T. viridarium, A. brassicicola and the host plant.

Albugo candida causing white rust disease is a serious threat in cultivation of oilseed Brassicas particularly Brassica juncea when inflorescence infection (staghead) is high. Stagheads carry oospores which are the perpetuating structures and primary source of inoculum in white rust disease. However, two aspects that still needs to be addressed in A. candida-B. juncea pathosystem are determination of optimum oospore germination conditions and oospore viability assessment. Thus, here we have described a protocol for optimum oospore germination in samples derived from naturally infected stagheads of B. juncea on β-glucuronidase aryl sulfatase (a type of snail gut enzyme) treatment. The results indicates that 3% enzyme concentration induces ≥ 50% of oospores to germinate at 7°C, 10°C and 13°C after incubating for 24–48 hours. We also determined viability of staghead-derived and seed lot derived oospores through plasmolysis (using 4 M sodium chloride) and trypan blue staining. The results indicate the significant superiority of plasmolysis method in detecting viable oospores of A. candida than trypan blue staining. The techniques described here can be utilised for carrying out studies on oospore mating behaviour, sexual recombination and assessment of oosporic inoculum viability in A. candida-B. juncea pathosystem.

Huanglongbing (HLB) is the most devastating citrus disease around the world. We have modelled HLB spread in scenarios with different populational levels of the main alternative host (Murraya paniculata) and Diaphorina citri Kuwayama (Asian citrus psyllid—ACP) vector of HLB-associated bacteria and the removal of HLB-symptomatic plants. A compartmental deterministic mathematical model was built for representing the HLB dynamics in the Recôncavo Baiano, Bahia State, Brazil. The model encompasses delays on latency and incubation disease periods and on the ACP nymphal stages. The simulations indicated that presence of alternative hosts at a low proportion would not play a crucial role in HLB dynamics in situations of poor ACP management, regardless of HLB-symptomatic plant eradication. Symptomatic citrus plants contribute more to increase the HLB incidence than the alternative host in scenarios without a suitable ACP management.

The majority of the crops are wasted owing to deficiency of transport, plant diseases and lack of storage facilities. Above 15% of crops are worn out in India owing to diseases and therefore it has turned out to be a main concern to be solved. This study introduces an advanced framework for plant disease detection by integrating enhanced image segmentation techniques with robust ensemble classification models. Our methodology begins with the pre-processing of plant leaf images using median filtering and Wiener denoising to reduce noise and enhance image quality. As the next step, the Improved Region Growing Algorithm (IRGA) is deployed for the segmentation of images. Then, features together with ‘Scale Invariant Feature Transform (SIFT), improved Binary Gabor Pattern (IBGP), Haralick features, color features like RGB Color Histogram, disease area and higher order statistical features (Entropy, Skewness, variance and kurtosis)’ are extracted. The improved independent component analysis (IICA) model is then used to choose the best attributes. Lastly, detection takes place using Ensemble classifiers (EC) including Neural Network (NN), modified effective squeeze and excitation block-based deep convolutional neural network (M-ESE-DCNN) and bi-directional gated recurrent unit (BI-GRU). Further, the DCNN weights are optimised via the Colliding Archimedes and Teamwork Algorithm (CA-TWA) model. For the best case with dataset 1, EC + CA-TWA got a high accuracy of 0.94, while EC + BOA, EC + DOX, EC + SSO, EC + TOA and EC + ArOA had lower accuracy. Furthermore, for all schemes, dataset 1 displays superior outputs to dataset 2 and dataset 3. Finally, an evaluation is done to validate this work.

Tobacco brown spot, caused by Alternaria alternata (Fries) Keissler, is one of the main leaf diseases of tobacco. It is common in the mature stage of tobacco but rarely occurs in the early growth stage. Therefore, it is also called ‘mature spot’ by tobacco farmers. However, the physiological and biochemical mechanisms underlying the responses of tobacco plants at different growth stages to A. alternata remain unclear. In this experiment, A. alternata was inoculated into two tobacco varieties, Yun87 and HongHuaDaJinYuan (HD), at the immature and mature stages respectively. The disease index, stomata, reactive oxygen species (OFR and H₂O₂) and main defence enzymes (SOD, POD, PPO, CAT and PAL) of tobacco at the two growth stages were analysed. Results showed that the disease index of tobacco plants in the immature stage was lower than that of tobacco plants at the mature stage at 5 days after inoculation. Stomatal opening was not significantly different between the immature and mature stages, but the stomatal apertures of HD and Yun87 decreased by 1%–2.33% at 12 h after inoculation. Moreover, the OFR and H₂O₂ contents in the mature stage were significantly higher than those in the immature stage. SOD activity was higher in the immature stage than in the mature stage, whereas POD activity was higher in the mature stage than in the immature stage. PPO, CAT and PAL activities were not significantly different between the immature and mature stages. Reactive oxygen species and defence enzymes began to respond at 12 h after inoculation. Specifically, SOD and PAL activities peaked at 12 h, whereas CAT activity peaked at 24 h. The response levels and rates of H₂O₂ content, SOD activity and PAL activity were higher in the immature stage than in the mature stage. Correlation analysis revealed that the strength of the effect of these factors was different. In general, tobacco resistance to brown spot disease was higher in the immature stage than in the mature stage, and this resistance was mediated by the response rates and levels of stomata, reactive oxygen species and defence enzymes.

Plant diseases are the major factors that affects the quality production as it affects or interrupts the plant's vital functions. The early detection of crop disease could assist farmers in implementing the right preventative measures at the right moment to eradicate it. The main goal of the ODHEPDC (Optimal Trained Deep Hybrid Ensemble of Classifier for Classification of Plant Disease) model is the classification of diseases leaf images. The primary step is to improve the input image by using the MF model to remove the noise. This is considered as the preprocessing step. Improved fuzzy clustering algorithm, leading to the identification of the regions, ROI as well as non-ROI. Next to this, the appropriate features are extracted to define the feature set that includes MPPT feature, PHOG feature, and MTP features as well. However, the curse of dimensionality is the greatest crisis in the classification problem, hence, improved feature level fusion is progressed, which is the simple concatenation of the extracted features. In this, the improved calculation of information gain ensures the reduction and fusion of feature set. The fused features are the inputs to ensemble classification model with the classifiers like CNN, RNN, and DBN classifiers, which gives the classified results. To boost up the performance of classification model, the Deep Maxout model in the ensemble is optimally trained by a new Bald Eagle Search Updated Pelican Optimization (BESUPO) Algorithm via optimal weights tuning as the model determines the final classification outcome. The validation results prove the disease classification performance via the given architecture than extant schemes.