Journal of Plant Diseases and Protection最新文献

Global agriculture is heavily dependent on sustainable plant protection. Worldwide, the concept of integrated pest management (IPM) is being followed. IPM utilizes a range of strategies, with chemical synthetic pesticides being employed only as a last resort. However, in agricultural practice, farmers continue to rely primarily on this option. To further reduce this dependence, new strategies are being sought to strengthen the use of biological control within the IPM approach including the identification of novel non-synthetic natural compounds. Here, we discuss and report on the state of the art in biological control research in areas such as biocontrol agents and application of ecological principles. These practices can help to establish sustainable plant protection systems, with the greatest impact achieved when they are used in appropriate combinations. We highlight the conditions that currently prevent or hinder the increased use of biocontrol measures. On the background of agroecological experiences, we discuss why additional advancements in plant protection practices are imperative to more effectively break the life cycles of pests, diseases and weeds. We emphasize the significance of a judicious application of chemical control technologies, adapted to local conditions. Additionally, we highlight the key role and expertise of operators in implementing these practices and their knowledge thereof.

Abstract

Plant leaf disease identification and classification are the most essential and demanding tasks in the agriculture field. In traditional researches, various automated detection technologies have been developed with the goal of more accurately identifying plant leaf disease. Nevertheless, it faces some problems related to complex mathematical modeling, increased time consumption, processing overhead, and mis-prediction results. Therefore, a novel probabilistic intermittent neural network and artificial jelly fish optimization-based plant leaf disease detection system is proposed in this paper. The proposed work aims to “make a new detection scheme to identify correctly plant leaf disease from the given dataset.” Here, the probabilistic intermittent neural network (PINN) classification technique is used to predict label as normal or affected by disease. If it is disease affected, the residual multi-scale Unet segmentation (RMUNet) segmentation technique is applied to segment the disease affected region. Finally, the simulation outcomes confirm the efficiency of the proposed leaf disease identification system under some variables.

Abstract

This study proposes the relationship between fungal bioaerosols and biotic stress on crops using a case study on wheat leaf rust reported from northern India. We sampled and quantified the size-resolved fungal bioaerosols using the next-generation sequencing technique from a wheat crop field during winter. Puccinia recondita, the fungal pathogen that causes wheat leaf rust, was identified during the study period. The pathogen is known for its frequent and widespread occurrence of new variants that causes disease-resistant crop varieties susceptible to infections. This reveals the need for frequent and systematic monitoring to prevent rust infection. In the current study, the size-resolved fungal bioaerosol characterization was linked to the dispersal properties of the fungal propagules, and using a theoretical dispersion model, originating source and the areas of high risk for wheat leaf rust infection were identified. Our findings may serve as a vital reference for crop pathologists, agro technologists, environmentalists, and policymakers to expand the investigation on the biotic stress caused by the invasion of fungal bioaerosols on various crops and to implement preventive measures to ensure global food security.

Phytotoxic activity of essential oil emulsions (EOEs) of aromatic plants such as Mentha piperita, Pelargonium graveolens, Matricaria chamomilla, Chrysopogon zizanioides, Pogostemon patchouli, Mentha arvensis, and aqueous extracts of Andrographis paniculata were evaluated for problematic weeds like Avena fatua and Phalaris minor, along with wheat (Triticum aestivum) as a test crop through laboratory bioassay studies during 2020–2021. The chemical composition of essential oils was analyzed through GC/GC–MS. Results of the laboratory bioassay revealed that EOEs of M. piperita, C. zizanioides, M. arvensis, and an aqueous extract of A. paniculata as pre-emergence bioherbicides strongly inhibited germination and seedling growth of the tested weed species in a dose dependent manner, with P. minor and A. fatua being selectively more sensitive than T. aestivum. Percent seed germination varied between treatments, ranging from 3.45–48.28%, 3.45–100%, and 4.55–90.91% among T. aestivum, A. fatua, and P. minor, respectively. Based on these results, these four treatments showing the highest phytotoxic activity were further evaluated through a pot culture study. The results showed that treatments with essential oil emulsions and aqueous extracts had a negative impact on seedling Vigor Index I and Vigor Index II. Comparing physiological processes in P. minor, like relative electrolyte leakages, essential oil of M. arvensis at concentration 1% + 8% (pre emergence + early post emergence) had the highest levels of 47.16%, followed by the value of 38.86% which was recorded in C. zizanioides at concentration 1% + 8% (pre emergence + early post emergence) and 22.80% reported in M. piperita at concentration 1% + 8% (pre emergence + early post emergence) treated plants. These levels were higher than those in the untreated control (10.13%). Indeed, at higher concentrations of each treatment, grain yield per plant decreased in the range of 7–29% with respect to the untreated control. The data on phytotoxicity rating showed that visible injury symptoms in T. aestivum plants were less severe as compared to the symptoms recorded in A. fatua and P. minor weeds. This is the first in-depth study to show that emulsions of essential oils from P. graveolens, C. zizanioides, and P. patchouli are phytotoxic. It is also the first time that M. arvensis and C. zizanioides have been shown to be pre-emergence bioherbicides in wheat. Hence, our study presents these EOEs as novel candidates of biological origin that can be used in sustainable weed management, especially in organic farming systems.

Plants exhibit a remarkable capacity to discern between self-inflicted damage, herbivore attacks, and mechanical harm through pattern recognition, detecting specific signals associated with each type of damage. Mechanical damage significantly influences plant defence responses against herbivorous insects. This study aimed to artificially activate the plant defence system and observe the performance of aphid (Myzus persicae Sulzer) (Hemiptera: Aphididae) and their parasitoid (Diaeretiella rapae M’Intosh) (Hymenoptera: Braconidae) on brassica plants. Mechanically damaged and undamaged plants were subjected to aphid infestation, and various parameters related to aphid and parasitoid performance, including adult survival, fecundity, aphid settlement, and oviposition behavior, were measured. Results revealed that plants with artificial damage exhibited greater resistance to aphids than undamaged plants. In the cage bioassay, there was a notable 17% reduction in aphid larviposition on damaged plants, with no significant impact on adult mortality. The aphid settlement bioassay demonstrated a significant 33% reduction in aphid settlement on damaged plants compared to undamaged ones. Conversely, mechanical damage increased parasitism behavior, leading to a substantial 32% increase in parasitoids’ oviposition preference on damaged plants. These findings highlight the significance of considering mechanical damage as a crucial factor in altering plant–insect interactions. The study suggests that mechanical damage could be a potential tool for plant protection in agricultural settings by significantly suppressing aphid performance and enhancing parasitoid behaviour.

In this study, a strain HN2-3 was isolated from a rhizosphere soil sample of Setaria viridis plants. The genome size of the strain HN2-3 was 5,966,659 bp, with a GC content of 62.47%. In addition, the strain HN2-3 contains a 31,784 bp plasmid pHN2-3. The strain HN2-3 was characterized as Pseudomonas capeferrum by the combination of 16S rRNA gene-based phylogenetic analysis and Type (Strain) Genome Server (TYGS). Moreover, the cyclic lipopeptide (CLP) produced from P. capeferrum HN2-3 was isolated and purified from the bacterial supernatant by acid-aided precipitation and high-performance liquid chromatography (HPLC). A purified CLP (1) was characterized as putisolvin by genome mining, bioinformatic analyses, high-resolution mass spectrometry (HR-MS) and high-resolution tandem mass spectrometry (HR-MS/MS). Further in vitro assays showed that 10 and 50 μM putisolvin-treated zoospore suspension of Phytophthora capsici displayed the significant difference on lysis ability of zoospores compared to 1 μM putisolvin-treated zoospores. In addition, planta assay has revealed that only 50 μM putisolvin-treated zoospore suspension of Phytophthora capsici displayed a significant decreasing on Phytophthora blight disease in cucumbers. Overall, this study has indicated that putisolvin produced from P. capeferrum HN2-3 could be developped as a biopesticide for the management of Phytophthora blight disease in cucumbers.

In this study, we investigated native soil antagonists and the exotic Bacillus thuringiensis (Bt) in their effectiveness against chili bacterial pathogens, specifically Pseudomonas syringae and Xanthomonas vesicatoria. Putative native soil antagonists were isolated through plating heat-shocked (60 °C) serial dilutions on nutrient agar plates. Among these isolates, two (SK, CM) were further characterized based on phenotypic and biochemical traits, revealing gram-positive and catalase-positive characteristics, while being negative for urease, oxidase, and arginine dihydrolase. To evaluate the efficacy of SK, CM, and exotic Bt against P. syringae and X. vesicatoria, a dual culture confrontational assay was conducted. SK, CM, and Bt were found to restrict the growth of X. vesicatoria, not P. syringae. Consequently, further tests were exclusively performed with X. vesicatoria. Chemical compatibility with copper sulfate, topsin M, and sodium benzoate was assessed using the poisoned food technique, revealing inhibitory effects on microbial growth except for topsin M. Antibiotic sensitivity testing (erythromycin, streptomycin, tetracycline, penicillin) of X. vesicatoria, SK, CM, and Bt was carried out using the disk diffusion method. Results indicated a minimum zone of inhibition (0 mm) against penicillin and a maximum (29 mm) against streptomycin. The efficacy of antagonists was also tested in planta using detached leaf and seedling inoculation methods. Pre-inoculating leaves of a susceptible chili cultivar with antagonists and subsequently re-inoculating with X. vesicatoria demonstrated that, except for the positive control, leaves remained asymptomatic after 7 days post-inoculation. A pot experiment involving soil drenching and foliar spray inoculation further confirmed that plants initially treated with antagonists exhibited resistance against subsequent pathogen application compared to the positive control. In conclusion, this pilot study revealed that native putative Bacillus spp. isolates (SK, CM) and commercial exotic Bt have the potential to counteract X. vesicatoria in both local and distant tissues.

The detection of urban tree canopy plays a crucial role in assessing the ecosystem of trees and reducing greenhouse gases in smart cities. This research proposes an intelligent model for detecting tree canopy in urban environments using aerial images and LiDAR data, leveraging convolutional neural networks (CNNs). The proposed models have been trained and evaluated in urban areas with vegetation in Qom city. To accomplish this, three datasets were utilized to train a single model. The first dataset derived from LiDAR data, achieved an accuracy of 88.05% with a loss of 0.341, indicating that the model made correct predictions with a high percentage but had some errors. Similarly, in the second dataset utilizing aerial image data, the algorithm achieved a higher accuracy of 90.04% with a lower loss of 0.298, suggesting improved performance with fewer mistakes. Lastly, in the third dataset, which incorporated data derived from both LiDAR and aerial images, the algorithm achieved an even higher accuracy of 91.05% with a lower loss of 0.276, indicating further enhancement in prediction accuracy and reduced errors. On the other hand, the third model demonstrates an average value of 94%, 83.1%, and 78.9% for completeness, correctness, and quality, respectively, in identifying tree canopies. Completeness pertains to the CNN's precision in detecting and extracting pertinent features from the input data, while correctness relates to the accuracy of the CNN's predictions. Furthermore, quality encompasses the overall performance and dependability of the model. This indicates that the integration of aerial images and digital surface model (DSM) data acquired from LiDAR, along with the utilization of convolutional Neural Networks (CNNs), enhances the outcomes compared to alternative models.

Efficient grapevine downy mildew control necessitates the implementation of anti-resistance strategies to ensure the ongoing efficacy of available substances and optimal disease control. With the gradual disappearance of multi-site fungicides from the market, reliance on single-site fungicides poses a long-term risk of selecting strains resistant to multiple modes of action. Challenges in disease management encompass selecting optimal spray programs and monitoring field population sensitivity. This study evaluated the efficacy of anti-resistance strategies, including two single-site fungicides (mandipropamid and oxathiapiprolin), on disease control and fungicide sensitivity through a combination of field trials and laboratory tests for the biological and molecular characterization of the pathogen populations over a three-year period (2019–2021). Mandipropamid, a cellulose synthase inhibitor, is used since a long time for downy mildew control, while oxathiapiprolin, an OxySterol Binding Protein homologue Inhibitor, was introduced recently. Field trials demonstrated effective disease control, even in the presence of mandipropamid-resistant strains (with G1105S/V mutations in PvCesA3) and revealed a pronounced selection and spread of resistance to both fungicides in the vineyard where disease pressure was higher. Characterizing pathogen strains remained a significant obstacle in sensitivity monitoring, hindering precise determination of resistance frequencies related to fungicide programs. Traditional techniques, in fact, lack the resolution required for high-throughput isolation and characterization of resistant individuals. To address this challenge, we propose utilizing flow cytometry and fluorescence-activated cell sorting on field sporangia populations, a method able to determine both the number of resistant isolates and isolate pathogen strains in a single assay.

Botrytis cinerea and Oidium neolycopersici represent two of the key fungal pathogens of tomato. In this study, 142 isolates were screened for their biocontrol potential against these pathogens. They were initially isolated from rhizospheric and from non-rhizospheric soil samples collected near healthy tomato plants grown in unheated greenhouses showing severe epidemics of grey mould in the Jijel region of Algeria. All the isolates were tested in vitro against B. cinerea using dual culture assays, and a subsample of 40 isolates (20 rhizospheric isolates and 20 non-rhizospheric isolates) was retained. The antagonistic effect of these candidates on spore germination of B. cinerea and their effect against O. neolycopersici and B. cinerea on tomato plants were then evaluated. The dual culture assays showed that non-rhizospheric bacteria were significantly more effective than rhizospheric bacteria in inhibiting the mycelial growth of B. cinerea. In planta, however, rhizospheric isolates showed significantly higher protective levels. This protective effect was significantly correlated to the ability of bacterial isolates to inhibit spore germination of B. cinerea. Taken together, these results allowed us to retain seven isolates with over 90% of efficacy against B. cinerea. These isolates were also able to protect tomato plants against O. neolycopersici, and they were identified as, P. argentinensis (SJ2), Serratia marcescens (SJ11), Pseudomonas lactis (SJ55), Pseudomonas veronii (RSAB3), Stenotrophomonas maltophilia (RTB17), Bacillus subtilis (SA14) and Bacillus toyonensis (SA87). This study showed promising results that could be exploited for a potential application of bacterial-based biocontrol agents efficient against both B. cinerea and O. neolycopersici.