From Genome to Field-Observation of the Multimodal Nematicidal and Plant Growth-Promoting Effects of Bacillus firmus I-1582 on Tomatoes Using Hyperspectral Remote Sensing

Resistance to Plant Parasites in Tomato Is Induced by Soil Enrichment with Specific Bacterial and Fungal Rhizosphere Microbiome

Commercial formulations of beneficial microbes have been used to enrich the rhizosphere microbiome of tomato plants grown in pots located in a glasshouse. These plants have been subjected to attacks by soil-borne parasites, such as root-knot nematodes (RKNs), and herbivores, such as the miner insect Tuta absoluta. The development of both parasites and the symptoms of their parasitism were restricted in these plants with respect to plants left untreated. A mixture, named in the text as Myco, containing plant growth-promoting rhizobacteria (PGPR), opportunistic biocontrol fungi (BCF), and arbuscular mycorrhizal fungi (AMF) was more effective in limiting pest damage than a formulation containing the sole AMF (Ozor). Therefore, Myco-treated plants inoculated with RKNs were taken as a model for further studies. The PGPR contained in Myco were not able to reduce nematode infection; rather, they worsened symptoms in plants compared with those observed in untreated plants. Therefore, it was argued that both BCF and AMF were the microorganisms that colonized roots and stimulated the plant immune system against RKNs. Beneficial fungi colonized the roots by lowering the activities of the defense supporting enzymes endochitinases and β-1,3-glucanase. However, as early as three days after nematode inoculation, these enzyme activities and the expression of the encoding pathogenesis-related genes (PR-2, PR-3) were found to be enhanced in roots with respect to non-inoculated plants, thus indicating that plants had been primed against RKNs. The addition of paclobutrazol, which reduces salicylic acid (SA) levels in cells, and diphenyliodonium chloride, which inhibits superoxide generation, completely abolished the repressive effect of Myco on nematode infection. Inhibitors of copper enzymes and the alternative cyanide-resistant respiration did not significantly alter resistance induction by Myco. When Myco-treated plants were subjected to moderate water stress and inoculated with nematodes, they retained numbers of developed individuals in the roots similar to those present in regularly watered plants, in contrast to what occurred in roots of untreated stressed plants that hosted very few individuals because of poor nutrient availability.

Comparison of Selected Dimensionality Reduction Methods for Detection of Root-Knot Nematode Infestations in Potato Tubers Using Hyperspectral Imaging

Hyperspectral imaging is a popular tool used for non-invasive plant disease detection. Data acquired with it usually consist of many correlated features; hence most of the acquired information is redundant. Dimensionality reduction methods are used to transform the data sets from high-dimensional, to low-dimensional (in this study to one or a few features). We have chosen six dimensionality reduction methods (partial least squares, linear discriminant analysis, principal component analysis, RandomForest, ReliefF, and Extreme gradient boosting) and tested their efficacy on a hyperspectral data set of potato tubers. The extracted or selected features were pipelined to support vector machine classifier and evaluated. Tubers were divided into two groups, healthy and infested with Meloidogyne luci. The results show that all dimensionality reduction methods enabled successful identification of inoculated tubers. The best and most consistent results were obtained using linear discriminant analysis, with 100% accuracy in both potato tuber inside and outside images. Classification success was generally higher in the outside data set, than in the inside. Nevertheless, accuracy was in all cases above 0.6.

Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.

Selection of Bacterial Strains for Control of Root-Knot Disease Caused by Meloidogyne incognita

Root-knot disease caused by Meloidogyne incognita leads to significant crop yield losses that may be aggravated by the association with pathogenic fungi and bacteria. Biological agents can be effectively used against the complex disease of root-knot nematode and pathogenic fungi. In this study, 35 bacterial strains were analyzed for their in vitro nematicidal, antagonistic and growth stimulation activities. Based on results from the in vitro assays, grow-box experiments on tomato and cucumber were carried out with the strain BZR 86 of Bacillus velezensis applied at different concentrations. Effects of B. velezensis BZR 86 on the development of root-knot disease were evaluated by recording root gall index, number of galls and number of eggs in egg masses. Application of B. velezensis BZR 86 noticeably decreased the development of root-knot disease on tomato and cucumber plants, as well as significantly increased growth and biomass of cucumber plants in accordance with bacterial concentration. This study seems to demonstrate that strain B. velezensis BZR 86 could be an additional tool for an environmentally safe control of root-knot disease on horticultural crops.

Ubiquitousness of Haloferax and Carotenoid Producing Genes in Arabian Sea Coastal Biosystems of India

This study presents a comparative analysis of halophiles from the global open sea and coastal biosystems through shotgun metagenomes (n = 209) retrieved from public repositories. The open sea was significantly enriched with Prochlorococcus and Candidatus pelagibacter. Meanwhile, coastal biosystems were dominated by Marinobacter and Alcanivorax. Halophilic archaea Haloarcula and Haloquandratum, predominant in the coastal biosystem, were significantly (p < 0.05) enriched in coastal biosystems compared to the open sea. Analysis of whole genomes (n = 23,540), retrieved from EzBioCloud, detected crtI in 64.66% of genomes, while cruF was observed in 1.69% Bacteria and 40.75% Archaea. We further confirmed the viability and carotenoid pigment production by pure culture isolation (n = 1351) of extreme halophiles from sediments (n = 410 × 3) sampling at the Arabian coastline of India. All red-pigmented isolates were represented exclusively by Haloferax, resistant to saturated NaCl (6 M), and had >60% G + C content. Multidrug resistance to tetracycline, gentamicin, ampicillin, and chloramphenicol were also observed. Our study showed that coastal biosystems could be more suited for bioprospection of halophiles rather than the open sea.

Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D- convolutional neural networks and random forest model

Clostridium sporogenes spores are used as surrogates for Clostridium botulinum, to verify thermal exposure and lethality in sterilization regimes by food industries. Conventional methods to detect spores are time-consuming and labour intensive. The objectives of this study were to evaluate the feasibility of using hyperspectral imaging (HSI) and deep learning approaches, firstly to identify dead and live forms of C. sporogenes spores and secondly, to estimate the concentration of spores on culture media plates and ready-to-eat mashed potato (food matrix). C. sporogenes spores were inoculated by either spread plating or drop plating on sheep blood agar (SBA) and tryptic soy agar (TSA) plates and by spread plating on the surface of mashed potato. Reflectance in the spectral range of 547-1701 nm from the region of interest was used for principal component analysis (PCA). PCA was successful in distinguishing dead and live spores and different levels of inoculum (10² to 10⁶ CFU/ml) on both TSA and SBA plates, however, was not efficient on the mashed potato (food matrix). Hence, deep learning classification frameworks namely 1D- convolutional neural networks (CNN) and random forest (RF) model were used. CNN model outperformed the RF model and the accuracy for quantification of spores was improved by 4% and 8% in the presence and absence, respectively of dead spores. The screening system used in this study was a combination of HSI and deep learning modelling, which resulted in an overall accuracy of 90-94% when the dead/inactivated spores were present and absent, respectively. The only discrepancy detected was during the prediction of samples with low inoculum levels (<10² CFU/ml). In summary, it was evident that HSI in combination with a deep learning approach showed immense potential as a tool to detect and quantify spores on nutrient media as well as on specific food matrix (mashed potato). However, the presence of dead spores in any sample is postulated to affect the accuracy and would need replicates and confirmatory assays.

Bacteria as Biocontrol Tool against Phytoparasitic Nematodes

Phytoparasitic nematodes cause severe damage and yield losses to numerous agricultural crops. Considering the revision of the EU legislation on the use of pesticides on agricultural crops, control strategies with low environmental impact are required. The approach based on the use of bacteria seems particularly promising as it also helps to reduce the applied amounts of chemicals and stabilize ecological changes. This paper gives an overview of the main types of bacteria that can be used as biological control agents against plant parasitic nematodes and their interrelationships with plants and other organisms. Many experiments have given positive results of phytoparasitic nematode control by bacteria, showing possible prospects for their application. In vitro, greenhouse and field experiments have shown that bacteria can regulate the development of ecto- and endoparasitic nematodes by different modes of action. Triggering the induction of plant defense mechanisms by bacteria is seen as the optimum tool because the efficacy of bacterial treatment can be higher than that of chemical pesticides or at least close to it. Moreover, bacterial application produces additional positive effects on growth stimulation, raises yields and suppresses other pathogenic microorganisms. Commercial formulations, both as single bacterial strains and bacterial complexes, are examined.

Functional Hyperspectral Imaging by High-Related Vegetation Indices to Track the Wide-Spectrum Trichoderma Biocontrol Activity Against Soil-Borne Diseases of Baby-Leaf Vegetables

Research has been increasingly focusing on the selection of novel and effective biological control agents (BCAs) against soil-borne plant pathogens. The large-scale application of BCAs requires fast and robust screening methods for the evaluation of the efficacy of high numbers of candidates. In this context, the digital technologies can be applied not only for early disease detection but also for rapid performance analyses of BCAs. The present study investigates the ability of different Trichoderma spp. to contain the development of main baby-leaf vegetable pathogens and applies functional plant imaging to select the best performing antagonists against multiple pathosystems. Specifically, sixteen different Trichoderma spp. strains were characterized both in vivo and in vitro for their ability to contain R. solani, S. sclerotiorum and S. rolfsii development. All Trichoderma spp. showed, in vitro significant radial growth inhibition of the target phytopathogens. Furthermore, biocontrol trials were performed on wild rocket, green and red baby lettuces infected, respectively, with R. solani, S. sclerotiorum and S. rolfsii. The plant status was monitored by using hyperspectral imaging. Two strains, Tl35 and Ta56, belonging to T. longibrachiatum and T. atroviride species, significantly reduced disease incidence and severity (DI and DSI) in the three pathosystems. Vegetation indices, calculated on the hyperspectral data extracted from the images of plant-Trichoderma-pathogen interaction, proved to be suitable to refer about the plant health status. Four of them (OSAVI, SAVI, TSAVI and TVI) were found informative for all the pathosystems analyzed, resulting closely correlated to DSI according to significant changes in the spectral signatures among health, infected and bio-protected plants. Findings clearly indicate the possibility to promote sustainable disease management of crops by applying digital plant imaging as large-scale screening method of BCAs' effectiveness and precision biological control support.