The fungus Fusarium oxysporum f. sp. radicis-lycopersici is the causal agent of tomato foot and root rot disease. The green fluorescent protein (GFP) was used to mark this fungus in order to visualize and analyze the colonization and infection processes in vivo. Transformation of F. oxysporum f. sp. radicis-lycopersici was very efficient and gfp expression was stable for at least nine subcultures. Microscopic analysis of the transformants revealed homogeneity of the fluorescent signal, which was clearly visible in the hyphae as well as in the chlamydospores and conidia. To our knowledge, this is the first report in which this is shown. The transformation did not affect the pathogenicity. Using confocal laser scanning microscopy, colonization, infection, and disease development on tomato roots were visualized in detail and several new aspects of these processes were observed, such as (i) the complete colonization pattern of the tomato root system; (ii) the very first steps of contact between the fungus and the host, which takes place at the root hair zone by mingling and by the attachment of hyphae to the root hairs; (iii) the preferential colonization sites on the root surface, which are the grooves along the junctions of the epidermal cells; and (iv) the absence of specific infection sites, such as sites of emergence of secondary roots, root tips, or wounded tissue, and the absence of specific infection structures, such as appressoria. The results of this work prove that the use of GFP as a marker for F. oxysporum f. sp. radicis-lycopersici is a convenient, fast, and effective approach for studying plant-fungus interactions.

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. Τhe study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.

Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary layers of information, such as spatial texture and estimated vegetation height from the UAV digital surface model. UAV multispectral images acquired in the visible and near-infrared parts of the spectrum were used as the main source of data, together with texture that was estimated for the image bands using a local variance filter. The digital surface model was created from structure from motion algorithms using the UAV image stereopairs. From this layer, the terrain elevation was estimated using a focal minimum filter followed by a low-pass filter. The plant height was computed by subtracting the terrain elevation from the digital surface model. Three classification algorithms (maximum likelihood, minimum distance and an object-based image classifier) were used to identify S. marianum from other vegetation using various combinations of inputs: image bands, texture and plant height. The resulting weed distribution maps were evaluated for their accuracy using field-surveyed data. Both texture and plant height have helped improve the accuracy of classification of S. marianum weed, increasing the overall accuracy of classification from 70% to 87% in 2015, and from 82% to 95% in 2016. Thus, as texture is easier to compute than plant height from a digital surface model, it may be preferable to be used in future weed mapping applications.

Biosolid application is an effective strategy, alternative to synthetic chemicals, for enhancing plant growth and performance and improving soil properties. In previous research, biosolid application has shown promising results with respect to tomato resistance against Fusarium oxysporum f. sp. radicis-lycopersici (Forl). Herein, we aimed at elucidating the effect of biosolid application on the plant–microbiome response mechanisms for tomato resistance against Forl at a molecular level. More specifically, plant–microbiome interactions in the presence of biosolid application and the biocontrol mechanism against Forl in tomato were investigated. We examined whether biosolids application in vitro could act as an inhibitor of growth and sporulation of Forl. The effect of biosolid application on the biocontrol of Forl was investigated based on the enhanced plant resistance, measured as expression of pathogen-response genes, and pathogen suppression in the context of soil microbiome diversity, abundance, and predicted functions. The expression of the pathogen-response genes was variably induced in tomato plants in different time points between 12 and 72 h post inoculation in the biosolid-enriched treatments, in the presence or absence of pathogens, indicating activation of defense responses in the plant. This further suggests that biosolid application resulted in a successful priming of tomato plants inducing resistance mechanisms against Forl. Our results have also demonstrated that biosolid application alters microbial diversity and the predicted soil functioning, along with the relative abundance of specific phyla and classes, as a proxy for disease suppression. Overall, the use of biosolid as a sustainable soil amendment had positive effects not only on plant health and protection, but also on growth of non-pathogenic antagonistic microorganisms against Forl in the tomato rhizosphere and thus, on plant–soil microbiome interactions, toward biocontrol of Forl.