Search Results (79)

This article provides a comprehensive overview of the development and application of statistical methods, process-based models, machine learning, and deep learning techniques in potato yield forecasting. It emphasizes the importance of integrating diverse data sources, including meteorological, phenotypic, and remote sensing data. Advances in computer technology have enabled the creation of more sophisticated models, such as mixed, geostatistical, and Bayesian models. Special attention is given to deep learning techniques, particularly convolutional neural networks, which significantly enhance forecast accuracy by analyzing complex data patterns. The article also discusses the effectiveness of other algorithms, such as Random Forest and Support Vector Machines, in capturing nonlinear relationships affecting yields. According to standards adopted in agricultural research, the Mean Absolute Percentage Error (MAPE) in the implementation of prediction issues should generally not exceed 15%. Contemporary research indicates that, through the use of advanced and accurate algorithms, the value of this error can reach levels of even less than 10 per cent, significantly increasing the efficiency of yield forecasting. Key challenges in the field include climatic variability and difficulties in obtaining accurate data on soil properties and agronomic practices. Despite these challenges, technological advancements present new opportunities for more accurate forecasting. Future research should focus on leveraging Internet of Things (IoT) technology for real-time data collection and analyzing the impact of biological variables on yield. An interdisciplinary approach, integrating insights from ecology and meteorology, is recommended to develop innovative predictive models. The exploration of machine learning methods has the potential to advance knowledge in potato yield forecasting and support sustainable agricultural practices. Full article

Starch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality. Full article

Barley, an ancient crop, was vital for early civilizations and has historically been served as food and beverage. Today, it plays a major role as feed for livestock. Breeding modern barley varieties for high yield and quality has created significant genetic erosion. This highlights the importance of tapping into genetic and genomic resources to develop new improved varieties that can overcome agricultural bottlenecks and increase barley yield. In the current study, 75 barley genotypes were evaluated for agro-morphological traits. The relationships among these traits were determined based on genotype by trait (GT) biplot analysis for two cropping years (2021 and 2022). This study was designed as a randomized complete block experiment with four replications. The variation among genotypes was found to be significant for all traits. The correlation coefficient and GT biplot revealed that grain yield (GY) was positively correlated with the number of grains per spike (NGS), the grain weight per spike (GW), and the thousand kernel weight (1000 KW). However, the test weight (TW) was negatively correlated with the heading date (HD). Hierarchical analysis produced five groups in the first year, four groups in the second year, and four groups over the average of two years. Genotypes by trait biplot analysis highlighted G25, G28, G61, G73, and G74 as promising high-yielding barley genotypes. This study demonstrated the effectiveness of the GT biplot as a valuable approach for identifying superior genotypes with contrasting traits. It is considered that this approach could be used to evaluate the barley genetic material in breeding programs. Full article

Planting geometry is one of the most important management practices that determine plant growth and yield of corn. The effects of eight planting geometries (35 × 23 cm, 40 × 21 cm, 45 × 19 cm, 50 × 18 cm, 55 × 17 cm, 60 × 16 cm, 65 × 15 cm, 70 × 15 cm) on plant growth and yields of three sweet corn hybrids (Argos F₁, Challenger F₁, Khan F₁) were investigated under Erzurum, Türkiye conditions in 2022 and 2023 years. Variance analysis of the main factors shows a highly significant effect on whole traits but in two-way interactions some of the traits were significant and in the three-way interactions, it was insignificant. As an average of years, the number of plants per hectare at the harvest varied between 92,307 (35 × 23 cm) and 120,444 (70 × 15 cm) according to the planting geometries. The highest marketable ear number per hectare (107,456), marketable ear yield (24,887 kg ha⁻¹), and fresh kernel yield (19,493 kg ha⁻¹) were obtained from the 40 × 21 cm planting geometry. The results showed that the variety Khan F₁ grown at 40 × 21 cm planting geometry obtained the highest marketable ear number (112,472), marketable ear yield (29,788 kg ha⁻¹), and fresh kernel yield (22,432 kg ha⁻¹). The plant density was positively correlated with marketable ear number (r = 0.904 **), marketable ear yield (r = 0.853 **), and fresh kernel yield (r = 0.801 **). The differences among the varieties were significant for the studied traits, except for plant density and kernel number per ear. In conclusion, the variety Khan F₁ should be grown at the 40 × 21 cm planting geometry to maximize yields under study area conditions without water and nutrient limitations. Full article

Climate change has intensified abiotic stresses, notably salinity, detrimentally affecting crop yield. To counter these effects, nanomaterials have emerged as a promising tool to mitigate the adverse impacts on plant growth and development. Specifically, zinc oxide nanoparticles (ZnO-NPs) have demonstrated efficacy in facilitating a gradual release of zinc, thus enhancing its bioavailability to plants. With the goal of ensuring sustainable plant production, our aim was to examine how green-synthesized ZnO-NPs influence the seedling growth of quinoa (Chenopodium quinoa L. Cv Atlas) under conditions of salinity stress. To induce salt stress, solutions with three different NaCl concentrations (0, 100, and 200 mM) were prepared. Additionally, Zn and ZnO-NPs were administered at four different concentrations (0, 50, 100, and 200 ppm). In this study, plant height (cm), plant weight (g), plant diameter (mm), chlorophyll content (SPAD), K/Na value, Ca/Na value, antioxidant enzyme activities (SOD: EU g⁻¹ leaf; CAT: EU g⁻¹ leaf; POD: EU g⁻¹ leaf), H₂O₂ (mmol kg⁻¹), MDA (nmol g⁻¹ DW), proline (µg g⁻¹ FW), and sucrose (g L⁻¹), content parameters were measured. XRD analysis confirmed the crystalline structure of ZnO nanoparticles with identified planes. Salinity stress significantly reduced plant metrics and altered ion ratios, while increasing oxidative stress indicators and osmolytes. Conversely, Zn and ZnO-NPs mitigated these effects, reducing oxidative damage and enhancing enzyme activities. This supports Zn’s role in limiting salinity uptake and improving physiological responses in quinoa seedlings, suggesting a promising strategy for enhancing crop resilience. Overall, this study underscores nanomaterials’ potential in sustainable agriculture and stress management. Full article

The article aims to show the attitudes and views of Polish residents on the problem of preserving the natural environment from the perspective of their place of residence. The need for research in this area stems from the insufficient number of available studies on this very important issue given the global environmental challenges we are facing. The research gap noted relates particularly to the aspects of engagement in environmental measures, knowledge levels, and motivations for conservation efforts by local citizens. Environmentally and socially responsible behavior is part of the concept of sustainable development. Empirical research covered a sample of 500 adult residents of Poland using the CAWI technique. The results showed that the vast majority of respondents noticed numerous problems in preserving the natural environment in their place of residence. According to respondents, the way to reduce these problems is to increase care for green areas, promote renewable energy sources, and strive to reduce waste. Moreover, the research results show that respondents take initiatives to segregate waste, save energy, and apply the zero-waste concept. The main reason for taking action to solve environmental problems is to preserve the environment for our children and future generations. The results of these studies showed that for men, pro-environmental activities are more important than for women. These findings are valuable for policymakers, local authorities, and fellow citizens. Full article

We explored the impact of strontium oxide nanoparticles (SrO-NPs), synthesized through a green method, on seedling growth of bread wheat in hydroponic systems. The wheat plants were exposed to SrO-NPs concentrations ranging from 0.5 mM to 8.0 mM. Various parameters, including shoot length (cm), shoot fresh weight (g), root number, root length (cm), root fresh weight (g), chlorophyll value (SPAD), cell membrane damage (%), hydrogen peroxide (H₂O₂) value (µmol/g), malondialdehyde (MDA) value (ng/µL), and enzymatic activities like ascorbate peroxidase (APX) activity (EU/g FW), peroxidase (POD) activity (EU/g FW), and superoxide dismutase (SOD) activity (U/g FW), were measured to assess the effects of SrO-NPs on the wheat plants in hydroponic conditions. The results showed that the SrO-NPs in different concentrations were significantly affected considering all traits. The highest values were obtained from the shoot length (20.77 cm; 0.5 mM), shoot fresh weight (0.184 g; 1 mM), root number (5.39; 8 mM), root length (19.69 cm; 0 mM), root fresh weight (0.142 g; 1 mM), SPAD (33.20; 4 mM), cell membrane damage (58.86%; 4 mM), H₂O₂ (829.95 µmol/g; 6 mM), MDA (0.66 ng/µl; 8 mM), APX (3.83 U/g FW; 6 mM), POD (70.27 U/g FW; 1.50 mM), and SOD (60.77 U/g FW; 8 mM). The data unequivocally supports the effectiveness of SrO-NPs application in promoting shoot and root development, chlorophyll levels, cellular tolerance, and the activation of enzymes in wheat plants. Full article

Throughout germination, which represents the initial and crucial phase of the wheat life cycle, the plant is notably susceptible to the adverse effects of drought. The identification and selection of genotypes exhibiting heightened drought tolerance stand as pivotal strategies aimed at mitigating these effects. For the stated objective, this study sought to evaluate the responses of distinct wheat genotypes to diverse levels of drought stress encountered during the germination stage. The induction of drought stress was achieved using polyethylene glycol at varying concentrations, and the assessment was conducted through the application of multivariate analysis and machine learning algorithms. Statistical significance (p < 0.01) was observed in the differences among genotypes, stress levels, and their interaction. The ranking of genotypes based on tolerance indicators was evident through a principal component analysis and biplot graphs utilizing germination traits and stress tolerance indices. The drought responses of wheat genotypes were modeled using germination data. Predictions were then generated using four distinct machine learning techniques. An evaluation based on R-square, mean square error, and mean absolute deviation metrics indicated the superior performance of the elastic-net model in estimating germination speed, germination power, and water absorption capacity. Additionally, in assessing the criterion metrics, it was determined that the Gaussian processes classifier exhibited a better performance in estimating root length, while the extreme gradient boosting model demonstrated superior performance in estimating shoot length, fresh weight, and dry weight. The study’s findings underscore that drought tolerance, susceptibility levels, and parameter estimation for durum wheat and similar plants can be reliably and efficiently determined through the applied methods and analyses, offering a fast and cost-effective approach. Full article

The food industry is striving for a sustainable development of thermal food processing. Smoking is an example of a process that has grown in popularity in recent years. There is a lack of systematic knowledge in the literature regarding this undervalued process, so the purpose of this review is to analyze the state of knowledge about the methods and technologies of smoking food products and their impact on changing the quality of essential food products. Therefore, a comprehensive review of the literature on smoking processes from the past two decades was conducted. The most essential components absorbed from smoke during smoking are polycyclic aromatic hydrocarbons (PAHs). In the present work, 24 PAHs are summarized, and the capability of 12 food products to absorb them is described. Analysis of the principal components of absorbed PAHs showed that some products from different groups exhibit a similar ability to absorb these compounds, mainly influenced by their physical properties. The pre-treatment practices of raw materials before smoking, the smoking raw materials used, and their quality parameters were characterized (along with the effects of smoking methods on selected product groups: fish, meats, and cheeses). In addition, the gap in research concerning the absorption of other components of smoke, e.g., phenols, alcohols, ketones, and aldehydes, which directly impact food quality, is indicated. Full article

The objective of this study was to comprehend the efficiency of wheat regeneration, callus induction, and DNA methylation through the application of mathematical frameworks and artificial intelligence (AI)-based models. This research aimed to explore the impact of treatments with AgNO₃ and Ag-NPs on various parameters. The study specifically concentrated on analyzing RAPD profiles and modeling regeneration parameters. The treatments and molecular findings served as input variables in the modeling process. It included the use of AgNO₃ and Ag-NPs at different concentrations (0, 2, 4, 6, and 8 mg L⁻¹). The in vitro and epigenetic characteristics were analyzed using several machine learning (ML) methods, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor classifier (KNN), and Gaussian processes classifier (GP) methods. This study’s results revealed that the highest values for callus induction (CI%) and embryogenic callus induction (EC%) occurred at a concentration of 2 mg L⁻¹ of Ag-NPs. Additionally, the regeneration efficiency (RE) parameter reached its peak at a concentration of 8 mg L⁻¹ of AgNO₃. Taking an epigenetic approach, AgNO₃ at a concentration of 2 mg L⁻¹ demonstrated the highest levels of genomic template stability (GTS), at 79.3%. There was a positive correlation seen between increased levels of AgNO₃ and DNA hypermethylation. Conversely, elevated levels of Ag-NPs were associated with DNA hypomethylation. The models were used to estimate the relationships between the input elements, including treatments, concentration, GTS rates, and Msp I and Hpa II polymorphism, and the in vitro output parameters. The findings suggested that the XGBoost model exhibited superior performance scores for callus induction (CI), as evidenced by an R² score of 51.5%, which explained the variances. Additionally, the RF model explained 71.9% of the total variance and showed superior efficacy in terms of EC%. Furthermore, the GP model, which provided the most robust statistics for RE, yielded an R² value of 52.5%, signifying its ability to account for a substantial portion of the total variance present in the data. This study exemplifies the application of various machine learning models in the cultivation of mature wheat embryos under the influence of treatments and concentrations involving AgNO₃ and Ag-NPs. Full article