Comparative investigations of plant-associated fungal communities (mycobiomes) in distinct habitats and under distinct climate regimes have been rarely conducted in the past. Nowadays, high-throughput sequencing allows routine examination of mycobiome responses to environmental changes and results at an unprecedented level of detail. In the present study, we analysed Illumina-generated fungal ITS1 sequences from European beech (Fagus sylvatica) originating from natural habitats at two different altitudes in the German Alps and from a managed tree nursery in northern Germany. In general, leaf-inhabiting mycobiome diversity and composition correlated significantly with the origin of the trees. Under natural condition the mycobiome was more diverse at lower than at higher elevation, whereas fungal diversity was lowest in the artificial habitat of the tree nursery. We further identified significant correlation of leaf chlorophylls and flavonoids with both habitat parameters and mycobiome biodiversity. The present results clearly point towards a pronounced importance of local stand conditions for the structure of beech leaf mycobiomes and for a close interrelation of phyllosphere fungi and leaf physiology.

Comparative simultaneous studies of environmental high-throughput sequencing (HTS) and cultivation of plant-associated fungi have rarely been conducted in the past years. For the present contribution, HTS and extinction culturing were applied for the same leaf samples of European beech (Fagus sylvatica) in order to trace both “real” environmental drivers as well as method-dependent signals of the observed mycobiomes. Both approaches resulted in non-overlapping community composition and pronounced differences in taxonomic classification and trophic stages. However, both methods revealed similar correlations of the fungal communities with local environmental conditions. Our results indicate undeniable advantages of HTS over cultivation in terms of revealing a good representation of the major functional guilds, rare taxa and biodiversity signals of leaf-inhabiting fungi. On the other hand our results demonstrate that the immense body of literature about cultivable endophytic fungi can and should be used for the interpretation of community signals and environmental correlations obtained from HTS studies and that cultivation studies should be continued at the highest standards, e.g. when sequencing facilities are not available or if such surveys are expanded into functional aspects with experiments on living isolates. Comparative simultaneous studies of environmental high-throughput sequencing (HTS) and cultivation of plant-associated fungi have rarely been conducted in the past years. For the present contribution, HTS and extinction culturing were applied for the same leaf samples of European beech (Fagus sylvatica) in order to trace both “real” environmental drivers as well as method-dependent signals of the observed mycobiomes. Both approaches resulted in non-overlapping community composition and pronounced differences in taxonomic classification and trophic stages. However, both methods revealed similar correlations of the fungal communities with local environmental conditions. Our results indicate undeniable advantages of HTS over cultivation in terms of revealing a good representation of the major functional guilds, rare taxa and biodiversity signals of leaf-inhabiting fungi. On the other hand our results demonstrate that the immense body of literature about cultivable endophytic fungi can and should be used for the interpretation of community signals and environmental correlations obtained from HTS studies and that cultivation studies should be continued at the highest standards, e.g. when sequencing facilities are not available or if such surveys are expanded into functional aspects with experiments on living isolates.

Salicinoid phenolic glycosides are common defence substances in salicaceous trees and specialist leaf beetles use these compounds for their own defence against predators. Salicinoids vary qualitatively and qualitatively in aspen (Populus tremula) and this variation has a genetic basis. The foliar endophyte mycobiome is plentiful and we hypothesised that it is related to plant genotype, potentially mediated by salicinoid composition, and that interactions with the leaf beetle Chrysomela tremula may alter this relationship. We studied these three-way interactions in controlled greenhouse experiments. Endophytic fungi were isolated from sterilised leaf tissues with and without beetle damage, and from beetles. We confirmed that endophyte composition was influenced by host genotype. Beetle activity added generalist morphs to the mycobiome that overrode the initial host association. Yeast-like genera (Cryptococcus and Rhodotorula) were isolated only from beetle-damaged tissues and from beetles, whereas fast-growing filamentous fungi dominated beetle-free control plants. Competition experiments between filamentous fungi of plant origin and beetle-related yeasts suggested interaction of both stimulating and inhibiting modes of action amongst the fungi. As a result, we detected examples of amensalism, commensalism, parasitism and competition between the morphs tested, but we found no evidence of mutualism, and consequently no co-evolutionary relationship could be demonstrated, between yeasts carried by beetles, host genotype and associated filamentous morphs. Endophyte studies are method-dependent and high-throughput sequencing technology best define the fungal mycobiome, culturing however continues to be a cheap way to provide fundamental ecological insights and it is also required for experimental studies.

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model’s performance. To address this, we propose using regularization and data augmentation techniques to enhance the model’s generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture.

The application of polyploidy in sustainable agriculture has already brought much appreciation among researchers. Polyploidy may occur naturally or can be induced in the laboratory using chemical or gaseous agents and results in complete chromosome nondisjunction. This comprehensive review described the potential of polyploidization on plants, especially its role in crop improvement for enhanced production and host-plant resistance development against pests and diseases. An in-depth investigation on techniques used in the induction of polyploidy, cytogenetic evaluation methods of different ploidy levels, application, and current research trends is also presented. Ongoing research has mainly aimed to bring the recurrence in polyploidy, which is usually detected by flow cytometry, chromosome counting, and cytogenetic techniques such as fluorescent in situ hybridization (FISH) and genomic in situ hybridization (GISH). Polyploidy can bring about positive consequences in the growth and yield attributes of crops, making them more tolerant to abiotic and biotic stresses. However, the unexpected change in chromosome set and lack of knowledge on the mechanism of stress alleviation is hindering the application of polyploidy on a large scale. Moreover, a lack of cost–benefit analysis and knowledge gaps on the socio-economic implication are predominant. Further research on polyploidy coupling with modern genomic technologies will help to bring real-world market prospects in the era of changing climate. This review on polyploidy provides a solid foundation to do next-generation research on crop improvement.