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Tomato (Solanum lycopersicum) is a major horticultural crop of high economic importance. Phelipanche and Orobanche genera (broomrapes) are parasitic weeds, constituting biotic stressors that impact tomato production. Developing varieties with tolerance to broomrapes has become imperative for sustainable agriculture. Solanum pennellii, a wild relative of cultivated tomato, has been utilized as breeding material for S. lycopersicum. In the present study, it is the first time that an in-depth analysis has been conducted for these two specific introgression lines (ILs), IL6-2 and IL6-3 (S. lycopersicum X S. pennellii), which were employed to identify genes and metabolic pathways associated with resistance against broomrape. Comparative transcriptomic analysis revealed a multitude of differentially expressed genes (DEGs) in roots, especially in the resistant genotype IL6-3, several of which were validated by quantitative PCR. DEG and pathway enrichment analysis (PEA) revealed diverse molecular mechanisms that can potentially be implicated in the host’s defense response and the establishment of resistance. The identified DEGs were mostly up-regulated in response to broomrape parasitism and play crucial roles in various processes different from strigolactone regulation. Our findings indicate that, in addition to the essential role of strigolactone metabolism, multiple cellular processes may be involved in the tomato’s response to broomrapes. The insights gained from this study will enhance our understanding and facilitate molecular breeding methods regarding broomrape parasitism. Moreover, they will assist in developing sustainable strategies and providing alternative solutions for weed management in tomatoes and other agronomically important crops.

Absract: The contemporary field of artificial intelligence has witnessed the emergence of Automated Machine Learning (AutoML) as a noteworthy advancement, promising the delivery of high-performance end-to-end machine learning pipelines with minimal user intervention. While AutoML has exhibited considerable efficacy in various computer vision applications, an unexplored realm remains concerning its application in proximal hyperspectral imaging. The combination of hyperspectral imaging with AutoML for classifying acclimation/stress response levels of horticultural crops is an innovative application nowadays. In this study, PyCaret, an open-source AutoML framework, and PLS1-DA were evaluated for broccoli drought acclimation/stress classification using hyperspectral data. The results revealed that PyCaret and PLS1-DA performed equally well, with PyCaret slightly outperforming PLS1-DA and achieving an accuracy and F1 score of 1.00 both when differentiating between control and drought onset or drought acclimated plants and when differentiating between all three classes. These findings underscore the substantial potential of AutoML and hyperspectral imaging, particularly in tasks related to plants’ water dynamics classification.

The tiger longicorn beetle, Xylotrechus chinensis Chevrolat (Coleoptera: Cerambycidae), has posed a significant threat to mulberry trees in Greece since its invasion in 2017, which may be associated with global warming. Detection typically relies on observing adult emergence holes on the bark or dried branches, indicating severe damage. Addressing pest threats linked to global warming requires efficient, targeted solutions. Remote sensing provides valuable, swift information on vegetation health, and combining these data with machine learning techniques enables early detection of pest infestations. This study utilized airborne multispectral data to detect infestations by X. chinensis in mulberry trees. Variables such as mean NDVI, mean NDRE, mean EVI, and tree crown area were calculated and used in machine learning models, alongside data on adult emergence holes and temperature. Trees were classified into two categories, infested and healthy, based on X. chinensis infestation. Evaluated models included Random Forest, Decision Tree, Gradient Boosting, Multi-Layer Perceptron, K-Nearest Neighbors, and Naïve Bayes. Random Forest proved to be the most effective predictive model, achieving the highest scores in accuracy (0.86), precision (0.84), recall (0.81), and F-score (0.82), with Gradient Boosting performing slightly lower. This study highlights the potential of combining remote sensing and machine learning for early pest detection, promoting timely interventions, and reducing environmental impacts.

In this study, three pea varieties (Pisum sativum), including one field pea variety ‘Dodoni’, and two varieties of garden peas ‘Early Onward’ and ‘Rondo’, were irradiated with 100 Gy gamma rays. The irradiated seeds were then cultivated in the experimental field of the Plant Breeding and Agricultural Experimentation Laboratory of the Agricultural University of Athens, in the period of 2021–2022, affording them the chance to reveal their full potential under natural environmental conditions. The M1 generation plants were compared to non-irradiated plants in terms of their agro-morphological traits. The results indicate a statistically significant difference on several traits: plant height, plant weight, dry biomass, pod length, and the number of seeds per pod. On the other hand, no significant difference was detected in: plant growth rate, the number of shoots and leaves per plant, pods and seed yield per plant, and 1000-seed weight. However, the three pea varieties did not seem to respond in the same way. The M1 generation plants of the two garden pea varieties (‘Early Onward’ and ‘Rondo’) showed a shorter plant height and pod length than their corresponding non-irradiated plants. Likewise, the number of seeds per pod for both ‘Early Onward’ and ‘Dodoni’ was lower for the mutant plants than it was for the plants used as the control. In contrast, mutant plants of the ‘Dodoni’ variety indicated a greater plant weight and dry biomass per plant compared to non-irradiated plants. Finally, the correlation found between agronomic traits was the same regardless of the treatment (control and mutant plants). The number of seeds per plant indicated a positive correlation with the pods per plant and plant weight. Similarly, the dry biomass was positively correlated with plant weight, while being negatively correlated with pod length. Thus, the results obtained in the present study clearly indicate that there were differences between seeds irradiated with gamma rays and non-irradiated seeds in terms of their overall performance and various quantitative pea traits, which should to be further investigated in M2 and the following generations. Peas have gathered a significant market interest and demand. Given their narrow genetic base, we employed gamma irradiation technology, which can be effectively integrated with omics technologies in future generations. This study underscores the necessity to explore performance characteristics and integrate them with quality traits. Our findings reveal that each generation exhibits unique attributes, and specifically, provide a valuable foundation for identifying valuable characteristics for future breeding programs.

Physical and morphological characteristics of Greek wheat landrace and cultivar seeds were evaluated, aiming to assess their bread- and pasta-making quality. Furthermore, the plant agromorphological traits of wheat landraces and cultivars were measured and correlated with seed physical properties. Fifteen Triticum spp. accessions, out of which four cultivars (two Triticum aestivum and two T. durum), nine landraces (two T. durum, five T. aestivum, one T. dicoccum and one T. polonicum) and two T. durum of unknown status were studied. Seventeen morphological plant and seed traits were measured based on UPOV descriptors. Ear emergence was earlier in cultivars than in landraces. The plant weight of the landraces was, in some cases, almost twice the cultivars’. Seed firmness (62.96–194.85N) was positively correlated with thousand kernel weight and volume (0.840 and 0.791, P < 0.05, respectively). Based on the agromorphological traits, cluster analysis clearly separated the species and the cultivars from the landraces, and the unknown status accessions were grouped with the cultivars. Cluster analysis with all measured traits set the wheat accessions in the following distinct groups: (1) T. durum cultivars including the unknown status accessions, (2) T. aestivum cultivars, and (3) T. aestivum and T. durum landraces. T. polonicum and T. dicoccum each formed a separate group. Seed physical properties of the analyzed Greek landraces indicated their suitability for bread and/or pasta making.

Within the framework of preserving and valorizing the rich grapevine germplasm of the Epirus region of Greece, indigenous grapevine (Vitis vinifera L.) cultivars were characterized and assessed for their resilience to abiotic stresses in the context of climate change. The cultivars ‘Debina’ and ‘Dichali’ displayed significant differences in their response to drought stress as judged by morpho-physiological analysis, indicating higher drought tolerance for Dichali. Hence, they were selected for further study aiming to identify genetic and epigenetic mechanisms possibly regulating drought adaptability. Specifically, self-rooted and heterografted on ‘Richter 110’ rootstock plants were subjected to two phases of drought with a recovery period in between. Gene expression analysis was performed for two stress-related miRNAs and their target genes: (a) miRNA159 and putative targets, VvMYB101, VvGATA-26-like, VvTOPLESS-4-like and (b) miRNA156 and putative target gene VvCONSTANS-5. Overall, grafted plants exhibited a higher drought tolerance than self-rooted plants, suggesting beneficial rootstock–scion interactions. Comparative analysis revealed differential gene expression under repetitive drought stresses between the two cultivars as well as between the self-rooted and grafted plants. ‘Dichali’ exhibited an up-regulation of most of the genes examined, which may be associated with increased tolerance. Nevertheless, the profound down-regulation of VvTOPLESS-4-like (a transcriptional co-repressor of transcription factors) upon drought and the concomitant up-regulation of miRNA159 highlights the importance of this ‘miRNA-target’ module in drought responsiveness. DNA methylation profiling using MSAP analysis revealed differential methylation patterns between the two genotypes in response to drought. Further investigations of gene expression and DNA methylation will contribute to our understanding of the epigenetic mechanisms underlying grapevine tolerance to drought stress.

Wood-boring insect pests pose a significant threat to orchards, potentially leading to tree mortality. In the initial stages of infestation, no visible symptoms are apparent, but as infestations progress, rapid and widespread symptoms emerge, resulting in accelerated tree decline. Therefore, the timely detection of early wood-boring insect symptoms is critical for effective pest control, necessitating advanced methods such as remote sensing. In this study, remote sensing is utilized to identify the early symptoms of peach flatheaded root borer (PFRB) infestation in trees. A multispectral sensor attached to a UAV captures aerial imagery data from stone fruit and pome fruit orchards. These data undergo processing in photogrammetric and GIS programs, where NDVI, NDRE, and the tree crown area are computed. On-site observations confirm PFRB infestations. Various machine-learning models, including logistic regression (LR), artificial neural network (NN), random forest (RF), and extreme gradient boosting (XGBoost), are compared using mean NDVI values, mean NDRE values, crown area, mean temperature, and mean relative humidity. Mean NDVI values emerge as the most crucial factor for predicting PFRB infestation across all machine-learning models. The XGBoost model proves the most effective, achieving an accuracy of 0.85, with marginal variations from the other tested models.

Broomrapes (Orobanche and Phelipanche spp.) are non-achlorophyllous parasitic plants belonging to the Orobanchaceae family, with some species evolving to infest agricultural crops, causing substantial economic losses. This study focuses on Orobanche and Phelipenche species prevalent in Tunisia, particularly Orobanche crenata, Orobanche foetida and Phelipanche ramosa, which pose a significant threat to legume crops and other agronomically important plants. These parasitic species cause severe damage before their aboveground appearance, making early detection and management crucial. Successful breeding programs targeting their hosts necessitate a comprehensive understanding of the genetic variability within different broomrape populations. A plethora of molecular markers, including RAPD, ISSR, AFLP, SSR and SNPs, were employed to evaluate the genetic diversity of Orobanche spp., mainly in Mediterranean countries. This research seeks to analyze the genetic variability and structure of thirty-four (34) Tunisian Orobanche and Phelipanche populations infesting various crops and wild plants. The results demonstrated a higher genetic differentiation within populations rather than between populations and no clear differentiation based on the geographic origins of the populations. By measuring the genetic diversity of a large number of broomrape populations that affect both wild species and crops, this study aims to support efforts toward establishing effective management approaches.

The Honeycomb Selection Design (HSD) is an innovative experimental method whose main feature is the even and systematic entry arrangement. Its systematicity, if combined with the absence of inter-plant competition that maximizes the phenotypic expression and differentiation of individual plants, enables the implementation of single-plant selection as early as the initial generations of genetic segregation, facilitating plant breeders to identify superior genotypes. Due to the specificity of entry allocation and the complexity of statistical data analysis, a specialized software becomes necessary. This article provides a detailed presentation of the ‘rhoneycomb’, a free and open-source R package concerning the construction, visualization, and analysis of HSDs.

Earthworms are important members of the soil fauna since they regulate fundamental soil functions such as organic matter breakdown, fertility, structure and water balance. However, so far, their community assemblages have been poorly investigated in Greece. In this context, the earthworm communities of forty five sites in Greece, belonging to three types of ecosystems—terrestrial water bodies, undisturbed (natural) sites and cultivated (agricultural and urban) fields—were investigated using the combined method of digging and hand sorting followed by the application of a 0.4% formaldehyde solution. Specific soil parameters and various environmental characteristics were examined as potential factors affecting the abundance and species richness. The results showed no statistically significant difference between ecosystem densities. The species number was significantly different between ecosystems, with the cultivated fields exhibiting richer communities, with a mean of 5.3 ± 0.6 species per site compared to the natural areas with 2.4 ± 0.5 species per site, while the water bodies showed intermediate numbers (3.6 ± 0.5 species per site). Finally, earthworm densities were positively correlated with species number and percentage vegetation cover and negatively with clay. These results may contribute to understanding how different land uses affect earthworm communities.

Grafting of elite cultivars onto tolerant rootstocks is an advanced strategy to increase tomato tolerance to sub-optimal temperature. However, a detailed understanding of adaptive mechanisms to sub-optimal temperature in rootstocks and scions of grafting combinations on a physiological and molecular level is lacking. Here, the commercial cultivar Kommeet was grafted either onto ‘Moneymaker’ (sensitive) or onto the line accession LA 1777 of Solanum habrochaites (tolerant). Grafted plants were grown in NFT-system at either optimal (25°C) or sub-optimal (15°C) temperatures in the root environment with optimal air temperature (25°C) for 22 days. Grafting onto the differently tolerant rootstocks caused differences in shoot fresh and dry weight, total leaf area and dry matter content of roots, in stomatal conductance and intercellular CO2 and guaiacol peroxidase activity but not in net photosynthesis, sugar, starch and amino acid content, lipid peroxidation and antioxidant enzyme activity. In leaves, comparative transcriptome analysis identified 361 differentially expressed genes (DEG) responding to sub-optimal root temperature when ‘Kommeet’ was grafted onto the sensitive but no when grafted onto the tolerant rootstock. 1509 and 2036 DEG responding to sub-optimal temperature were identified in LA 1777 and ‘Moneymaker’ rootstocks, respectively. In tolerant rootstocks down-regulated genes were enriched in main stress-responsive functional categories and up-regulated genes in cellulose synthesis suggesting that cellulose synthesis may be one of the main adaptation mechanisms to long-term sub-optimal temperature. Down-regulated genes of the sensitive rootstock showed a similar response, but functional categories of up-regulated genes pointed to induced stress responses. Rootstocks of the sensitive cultivar Moneymaker showed in addition an enrichment of up-regulated genes in the functional categories fatty acid desaturation, phenylpropanoids, biotic stress, cytochrome P450 and protein degradation, indicating that the sensitive cultivar showed more transcriptional adaptation to low temperature than the tolerant cultivar that did not show these changes. Mainly defense-related genes were highly differentially expressed between the tolerant and sensitive rootstock genotypes under sub-optimal temperature in the root environment. These results provide new insights into the molecular mechanisms of long-term sub-optimal temperature tolerance of tomato.