An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture.

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

Data Science and Plant Metabolomics.

The study of plant metabolism is one of the most complex tasks, mainly due to the huge amount and structural diversity of metabolites, as well as the fact that they react to changes in the environment and ultimately influence each other. Metabolic profiling is most often carried out using tools that include mass spectrometry (MS), which is one of the most powerful analytical methods. All this means that even when analyzing a single sample, we can obtain thousands of data. Data science has the potential to revolutionize our understanding of plant metabolism. This review demonstrates that machine learning, network analysis, and statistical modeling are some techniques being used to analyze large quantities of complex data that provide insights into plant development, growth, and how they interact with their environment. These findings could be key to improving crop yields, developing new forms of plant biotechnology, and understanding the relationship between plants and microbes. It is also necessary to consider the constraints that come with data science such as quality and availability of data, model complexity, and the need for deep knowledge of the subject in order to achieve reliable outcomes.

Oxidative Status of Medicago truncatula Seedlings after Inoculation with Rhizobacteria of the Genus Pseudomonas, Paenibacillus and Sinorhizobium.

An increasing number of scientists working to raise agricultural productivity see the potential in the roots and the soil adjacent to them, together with a wealth of micro-organisms. The first mechanisms activated in the plant during any abiotic or biotic stress concern changes in the oxidative status of the plant. With this in mind, for the first time, an attempt was made to check whether the inoculation of seedlings of the model plant Medicago truncatula with rhizobacteria belonging to the genus Pseudomonas (P. brassicacearum KK5, P. corrugata KK7), Paenibacillus borealis KK4 and a symbiotic strain Sinorhizobium meliloti KK13 would change the oxidative status in the days following inoculation. Initially, an increase in H2O2 synthesis was observed, which led to an increase in the activity of antioxidant enzymes responsible for regulating hydrogen peroxide levels. The main enzyme involved in the reduction of H2O2 content in the roots was catalase. The observed changes indicate the possibility of using the applied rhizobacteria to induce processes related to plant resistance and thus to ensure protection against environmental stress factors. In the next stages, it seems reasonable to check whether the initial changes in the oxidative state affect the activation of other pathways related to plant immunity.

IoT in Water Quality Monitoring-Are We Really Here?

The Internet of Things (IoT) has become widespread. Mainly used in industry, it already penetrates into every sphere of private life. It is often associated with complex sensors and very complicated technology. IoT in life sciences has gained a lot of importance because it allows one to minimize the costs associated with field research, expeditions, and the transport of the many sensors necessary for physical and chemical measurements. In the literature, we can find many sensational ideas regarding the use of remote collection of environmental research. However, can we fully say that IoT is well established in the natural sciences?

Effects of Rhizosphere Bacteria on Strawberry Plants (Fragaria × ananassa Duch.) under Water Deficit.

Due to the observed climate warming, water deficiency in soil is currently one of the most important stressors limiting the size and quality of plant crops. Drought stress causes a number of morphological, physiological, and biochemical changes in plants, limiting their growth, development, and yield. Innovative methods of inducing resistance and protecting plants against stressors include the inoculation of crops with beneficial microorganisms isolated from the rhizosphere of the plant species to which they are to be applied. The aim of the present study was to evaluate 12 different strains of rhizosphere bacteria of the genera Pantoea, Bacillus, Azotobacter, and Pseudomonas by using them to inoculate strawberry plants and assessing their impact on mitigating the negative effects of drought stress. Bacterial populations were assessed by estimates of their size based on bacterial counts in the growth substrate and with bioassays for plant growth-promoting traits. The physiological condition of strawberry plants was determined based on the parameters of chlorophyll fluorescence. The usefulness of the test methods used to assess the influence of plant inoculation with rhizosphere bacteria on the response of plants growing under water deficit was also evaluated. A two-factor experiment was performed in a complete randomization design. The first experimental factor was the inoculation of plant roots with rhizosphere bacteria. The second experimental factor was the different moisture content of the growth substrate. The water potential was maintained at -10 to -15 kPa under control conditions, and at -40 to -45 kPa under the conditions of water deficit in the substrate. The tests on strawberry plants showed that the highest sensitivity to water deficiency, and thus the greatest usefulness for characterizing water stress, was demonstrated by the following indices of chlorophyll "a" fluorescence: FM, FV, FV/FM, PI, and Area. Based on the assessment of the condition of the photosynthetic apparatus and the analysis of chlorophyll "a" fluorescence indices, including hierarchical cluster analysis, the following strains of rhizosphere bacteria were found to have favorable effects on strawberry plants under water deficit: the Bacillus sp. strains DLGB2 and DKB26 and the Pantoea sp. strains DKB63, DKB70, DKB68, DKB64, and DKB65. In the tests, these strains of Bacillus sp. exhibited a common trait-the ability to produce siderophores, while those of Pantoea sp. were notable for phosphate mobilization and ACCD activity.

Toxic Elements and Mineral Content of Different Tissues of Endemic Edible Snails (Helix vladika and H. secernenda) of Montenegro.

The objective of the present study is to determine the differences between the mineral content of various organs of Helix vladika and H. secernenda, the two most abundant edible snail species in Montenegro. The bioaccumulation of 12 examined elements (zinc, manganese, copper, aluminum, cadmium, lead, nickel, iron, chromium, lithium, selenium and mercury) was determined in the hepatopancreas, albumen gland, digestive tract, reproductive system, mantle, foot and shell from three sampling sites (Biogradska Gora, Niksic and Malesija). The examined populations of H. vladika and H. secernenda showed a significant difference in their lithium and selenium contents. The levels of the most examined metals (Zn, Mn, Cu, Al, Cd, Pb, Se and Hg) varied significantly among organs. The digestive tract and hepatopancreas tend to bioaccumulate selenium and cadmium. The general mean concentration of cadmium in the examined snail tissues exceeded the maximum allowable level at the Biogradska Gora and Malesija sites. Therefore, the use of the Montenegrin edible snails collected from the wilderness for human consumption seems to be limited by their higher bioaccumulation capacity for toxic elements such as cadmium.