This study explores the application of machine learning and genetic algorithm to model and optimize the spraying process of blueberry plants (Vaccinium corymbosum L.). The research aimed to determine the relationship between sprayer operating parameters (nozzle type, liquid pressure, and driving speed), weather conditions (temperature, humidity, and wind speed), and spray quality, specifically the coverage of various plant surfaces. Three ML methods, namely Gradient Boosting Regressor, Support Vector Regressor, and Extreme Gradient Boosting were evaluated to develop predictive models based on experimental data collected using water-sensitive papers. The results indicate that all models achieved high accuracy, with the SVR and XGBoost models being particularly effective, showing a MAPE error as low as 4

Abstract Soil compaction is a significant factor affecting crop yield; however, traditional assessment methods are labor-intensive and costly. To address this issue, rapid and cost-effective estimation techniques are needed. Geophysical methods are commonly used to predict various soil parameters, yet assessing parameters related to soil compaction remains challenging. Thus, integrating geophysical methods with machine learning presents a promising alternative to pedophysical models. This study examined how machine learning techniques — such as neural networks, support vector machines, and extreme gradient boosting — can predict penetration resistance, a key indicator of soil compaction. The models were developed using geophysical data from Geonics EM38 and Veris 3100 scanners, along with soil texture-related coefficients. Penetration resistance was measured at various soil depths ranging from the surface to 0.5 m. The most accurate models were achieved with the support vector machine algorithm for penetration resistance analyzed in soil layers from 0 to 0.4 m. These models utilized soil apparent electrical conductivity and magnetic susceptibility measured with the EM38 scanner at a depth of 0.5 m, electrical conductivity from the Veris 3100 scanner, and the soil texture coefficient as independent variables. For these models, the mean percentage error (MAPE) for the test data fell between 12 and 23%, while the correlation coefficient R ranged from 0.66 to 0.79. The resulting models can be practically applied to plan tillage at various depths, optimizing fuel consumption without compromising soil conditions necessary for plant root system development.

Sustainable agriculture aims to increase agricultural productivity while minimising negative environmental impacts [...]

Plant infestations cause significant economic losses in agriculture, necessitating rapid and accurate detection for optimized agrotechnical operations and reduced environmental pollution. This study addresses this challenge by proposing a customized convolutional neural network (CNN) architecture for detecting corn leaf worm infestations in maize. The research focuses on developing unique CNN models through extensive experimentation, systematically adjusting hyperparameters like optimizers, filter numbers, and kernel sizes. The study’s main contributions include the design of an accurate CNN classifier, and its implementation in a user-friendly smartphone application. The research highlights the importance of hyperparameter tuning in CNN performance, demonstrating that optimal configurations lead to high accuracy (up to 95% for accuracy, precision, recall, specificity, and F1-score). While the current model focuses on a single pest, the findings underscore the potential of custom CNN classifiers in vision systems for automated crop inspection, offering a promising solution for minimizing crop losses and the environmental impact of chemical plant protection products.

The practice of precision farming is contingent upon a comprehensive understanding of the spa-tial variability of a multitude of physical and chemical soil parameters. The acquisition of knowledge regarding soil parameters necessitates the undertaking of soil sampling and subsequent analysis, a process that is inherently labour-intensive and time-consuming. Consequently, preci-sion farming employs the identification of homogeneous field regions through the utilisation of scanning techniques, with the objective of ascertaining soil electrical characteristics, including electrical conductivity and magnetic susceptibility. The objective of this study was to attempt to predict soil compaction based on selected electrical parameters. In order to predict compaction, machine learning methods, namely decision tree and support vector regression were employed. The highest R-value of 0.87 was obtained for the decision tree model and soil layer 0.1-0.2 m for the training set. For the test set, the highest R-value of 0.85 was obtained for soil layer 0.1-0.2 m and the support vector regression model, which also had the lowest MAPE error value of 11.31%. The prediction of soil compaction using electrical soil parameters based on machine learning methods represents a promising avenue of research.

The agricultural sector is one of the most significant sectors of the global economy, yet it is concurrently a highly energy-intensive industry. The issue of optimizing field operations in terms of energy consumption is therefore a key consideration for sustainable agriculture, and the solution to this issue leads to both environmental and financial benefits. The aim of this study was to estimate energy consumption during soil cultivation using geophysical scanning data and machine learning (ML) algorithms. This included determining the optimal set of independent variables and the most suitable ML method. Soil parameters such as electrical conductivity, magnetic susceptibility, and soil reflectance in infrared spectra were mapped using data from Geonics EM-38 and Veris 3100 scanners. These data, along with soil texture, served as inputs for predicting fuel consumption and field productivity. Three machine learning algorithms were tested: support vector machines (SVMs), multilayer perceptron (MLP), and radial basis function (RBF) neural networks. Among these, SVM achieved the best performance, showing a MAPE of 4% and a strong correlation (R = 0.97) between predicted and actual productivity values. For fuel consumption, the optimal method was MLP (MAPE = 4% and R = 0.63). The findings demonstrate the viability of geophysical scanning and machine learning for accurately predicting energy use in tillage operations. This approach supports more sustainable agriculture by enabling optimized fuel use and reducing environmental impact through data-driven field management. Further research is needed to obtain training data for different soil parameters and agrotechnical treatments in order to develop more universal models.

Lonicera caerulea L. - well - known in Poland as Kamchatka berry, has been gaining increasing popularity in recent years. The tests carried out on newly established Japanese haskap clones aimed at demonstrating the suitability of the fruits for mechanical harvesting and storage. This study focused on evaluation of mechanical properties and assessment of three distinct machine learning techniques to create predictive models that elucidate the connection between key mechanical attributes of the fruit and storage conditions of L. caerulea . The average force needed to puncture the fruits skin and flesh of L. caerulea var. emphyllocalyx varieties is 16.91% higher than that required for the tested L. caerulea var. kamtschatica varieties. L. caerulea var. emphyllocalyx fruits exhibited a significantly higher respiration rate, with C₂H₄ and CO₂ levels during storage being 25.5% and 10.5% higher, respectively, compared to L. caerulea var. kamtschatica varieties. The machine learning algorithms tested yielded accurate models for deformation and energy prediction. The mean absolute percentage error (MAPE) of these models was determined to be between 14.87 and 20.65%. Models with significantly lower accuracy were obtained for force prediction, with the MAPE reaching 28.95% for L. var. kamtschatica fruit and 42.33% for L. emphyllocalyx fruit. The cultivation and improvement of Lonicera caerulea L. varieties is of great importance for the advancement of mechanized harvesting methods and the development of improved storage technologies for this species. The creation of machine learning methods will facilitate the development of predictive models that can serve as a predictive tool for the relationship between selected mechanical properties

The purpose of the study was to determine the effect of driving speed and liquid pressure on the average coverage rate and unevenness coefficient using selected standard and air induction flat fan nozzles. The study was conducted using a spraying device that functioned like a self-propelled sprayer. Based on the test results, it was found that the highest average coverage was obtained for single standard flat fan nozzles. On the other hand, greater uniformity was observed with standard nozzles at 0.4 MPa.

Japanese quince fruits (Chaenomeles L.) are a valuable source of bioactive compounds, including polyphenolic compounds, vitamins, macro- and microelements. By using Japanese quince fruit, you can fortify wheat beers and influence the qualitative and sensory values as well as the content of bioactive compounds in the finished product. Sensory evaluation showed that wheat beers with the addition of nonozonated Japanese quince fruits were characterized by a balanced taste and aroma (overall impression), but all wheat beers obtained were characterized by high sensory values, and the ozonation process had a positive effect on the microbiological stability of both fruits and beers with the addition of these fruit. Physico-chemical analysis of wheat beers showed that beers enriched with Japanese quince fruit were characterized by high total acidity, respectively: average values of 4.95 (fruit-free) and 6.66 (0.1 M NaOH/100 mL), higher energy value, and, especially beers enriched with ozonated Japanese quince fruit and high total polyphenol content. It was shown that wheat beers enriched with quince fruit contained mainly chlorogenic acid at an average level of 4.35 and 5.42 mg/L (for ozonated and nonozonated fruit, respectively). Electrical properties (impedance and capacity) allowed the differentiation of the analyzed wheat beers. Based on the conducted research, it appears that wheat beers enriched with Japanese quince fruit may constitute a new trend in the brewing industry.

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.

Effective spraying is essential for modern agricultural production, to ensure a high-quality and abundant harvest. Simultaneously, it is crucial to minimise the negative impact of crop protection products on the environment. To achieve this, it is necessary to implement the appropriate technical and technological parameters for the treatment and to consider the conditions under which the treatment is carried out. The aim of this study was to determine the relationship between the speed of the sprayer, the pressure of the liquid, and the type of nozzles, as well as air temperature and wind speed, in terms of the degree of coverage of the sprayed surfaces. The degree of coverage was analysed by spraying water-sensitive papers placed on the artificial plant, positioned to obtain horizontal and vertical surfaces. The study found that standard single flat fan nozzles provided greater coverage on upper horizontal surfaces, while standard nozzles were more effective for vertical transverse approach surfaces at lower fluid pressures and travel speeds. Neural networks were used to develop models of the relationships studied. Models with high accuracy for the validation data set were obtained in the case of the coverage of the vertical transverse leaving surface and the upper level surface (R = 0.93 and R = 0.86). These models were used to determine the optimum values of the technical parameters of the spraying process under the selected weather conditions. The maximum spray coverage (41.49%) was predicted for the XR nozzle under the following conditions: pressure = 200 kPa, driving speed = 1.4 m·s−1, temperature = 21.73 °C and wind speed = 0.32 m·s−1. Based on the sensitivity analysis of the neural models it was found that the greatest effect on the coverage of the vertical transverse leaving surface was observed for temperature and the coverage of the upper level surface was mostly influenced by driving speed.

In this study, selected mechanical properties of fruits of six varieties of Japanese quince (Chaenomeles japonica) were investigated. The influence of their storage time and the applied ozone at a concentration of 10 ppm for 15 and 30 min on water content, skin and flesh puncture force, deformation to puncture and puncture energy was determined. After 60 days of storage, the fruits of the tested varieties showed a decrease in the average water content from 97.94% to 94.39%. No influence of the ozonation process on the change in water content in the fruits was noted. The tests showed a significant influence of ozonation and storage time on the increase in the punch puncture force of the skin and flesh, deformation and puncture energy of the fruits. In order to establish the relationship between storage conditions for various varieties and selected mechanical parameters, a novel machine learning method was employed. The best model accuracy was achieved for energy, with a MAPE of 10% and a coefficient of correlation (R) of 0.92 for the test data set. The best metamodels for force and deformation produced slightly higher MAPE (12% and 17%, respectively) and R of 0.72 and 0.88.

The study examined selected biometric and mechanical properties of fruits of three varieties of red currant (Ribes rubrum L.) from organic cultivation. The influence of the harvest date of red currant fruits, their storage time, and the use of ozone at a concentration of 10 ppm for 15 and 30 min on the water content, volume, and density, as well as the destructive force and the apparent modulus of elasticity, were determined. Fruits harvested at harvest maturity were characterized by a much larger volume and lower water content compared to fruits harvested seven days earlier. The ozonation process, regardless of the harvest date, resulted in a reduction in volume, density, and humidity. After 15 days of storage, the fruits of the tested varieties showed a decrease in the average water content from 86.15% to 83.79%. The tests showed a decrease in the destructive force and the apparent modulus of elasticity, the average value of which for fresh fruit was 76.98 ± 21.0 kPa, and after 15 days of storage, it decreased to 56.34 ± 15.96 kPa. The relationships between fruit-related parameters, harvesting, and storage conditions and fruit strength characteristics were modeled with the use of neural networks and support vector machines. These relationships are complex and nonlinear, and therefore, machine learning is usually more relevant than the traditional methods of modeling. For evaluation of the performance of the models, statistical parameters such as the coefficient of correlation (R), root-mean-squared error (RMSE), and generalization ability coefficient (GA) were used. The best models for the prediction of an apparent modulus of elasticity were developed with the use of ANNs. These models can be used in practice because the correlation between expected and predicted values was in the range 0.78–0.82, RMSE was in the range 13.38–14.71, and generalization ability was excellent. A significantly lower accuracy was achieved for models with a destructive force as the output parameter (R ≤ 0.6).

One of the best sources of antioxidant and health-promoting bioactive substances is the fruit of V. corymbosum. A potent oxidizing agent, ozone (O3), can effectively eliminate bacteria. The application of ozone gas to V. corymbosum fruit during storage had a favorable impact on the fruit’s phenolic component and sugar content in the current investigation. After 7 days of storage, phenolic content in all highbush blueberry cultivars and clones tested increased on average by 28.60%, including anthocyanins by 34%. After 14 days of storage, an average increase of 16.50% in phenolic compounds was observed, including a 20.53% increase in anthocyanins. Among all the tested varieties, clone BOR-21 treated with a dose of 0.01 mL·L−1 ozone for 30 min after 14 days had the highest TPC—143.73 mg·100 g−1 f.w. The sugar content of berries treated with a dose of 0.01 mL·L−1 ozone for 30 min, on day 7 and day 14 of storage increased by 9.2% and 6.3%, respectively. On day 7, the highest amount of total sugar (22.74 g·100 g−1) was observed in Duke cultivar after being exposed to 0.01 mL·L−1 ozone for 15 min. The ozonation treatments enhanced the fruit’s saturation with nutrients, which raises the fruit’s value as food.

Background: In the last days of December 2020, the SARS-CoV-2 virus vaccine BNT162b2 (Comirnaty, Pfizer-BioNTech) was introduced, for the first time, for wide use in Poland. According to the vaccination schedule, healthcare workers were the first to receive the vaccine. The aim of this study was to analyse the attitudes of those who were determined to be vaccinated, with particular reference to their concerns, attitudes towards vaccination advocacy and sources of knowledge on vaccination, as well as the incidence of adverse reactions. Methods: The study had a three-stage design. Respondents completed a self-administered questionnaire before receiving the 1st and 2nd vaccine doses and 2 weeks after receiving the 2nd dose. A total of 2247 responses were obtained (1340 responses in the first stage, 769 in the second and 138 in the third). Results: The main source of knowledge on vaccination was the Internet (32%; n = 428). Of the respondents, 6% (n = 86) reported anxiety before the 1st dose of the vaccine, which increased to 20% (n = 157) before the 2nd dose. A declaration of willingness to promote vaccination among their families was made by 87% (n = 1165). Among adverse reactions after the 1st dose of the vaccine, respondents most frequently observed pain at the injection site (n = 584; 71%), fatigue (n = 126; 16%) and malaise (n = 86; 11%). The mean duration of symptoms was 2.38 days (SD 1.88). After the 2nd dose of vaccine, similar adverse reactions—pain at the injection site (n = 103; 75%), fatigue (n = 28; 20%), malaise (n = 22; 16%)—predominated among respondents. Those who declared having had a SARS-CoV-2 virus infection (p = 0.00484) and with a history of adverse vaccination reactions (p = 0.00374) were statistically more likely to observe adverse symptoms after vaccination. Conclusions: Adverse postvaccinal reactions are relatively common after Comirnaty vaccination but are usually mild and transient in nature. It is in the interest of public health to increase the knowledge of vaccine safety.

Waste management is one of most important challenges in environmental protection. Much effort is put into the development of waste treatment methods for further use. A serious problem is the treatment of municipal sewage sludge. One method that is useful for this substrate is composting. However, it is reasonable to compost a sewage sludge mixed with other substrates, such as maize straw. To carry out the composting process properly, it is necessary to control some parameters, including the total solids and volatile solids content in the composted mixture. In this paper, a method for the determination of the total solids and volatile solids content based on image analysis and neural networks was proposed. Image analysis was used for the determination of the colour and texture parameters. The three additional features describing the composted material were percentage of sewage sludge, type of maize straw, and stage of compost maturity. The neural models were developed based on various combinations of the input parameters. For both the total solids and volatile solids content, the most accurate models were obtained using all input parameters, including 30 parameters for image colour and texture and three features describing the composted material. The uncertainties of the developed models, expressed by the MAPE error, were 2.88% and 0.59%, respectively, for the prediction of the total solids and volatile solids content.

The role of disturbed immunoglobulin content during recurrent respiratory tract infections (RTI) might escape recognition in practical children's diagnostics. This study aims to investigate the potential changes in serum impedance caused by a constellation of decreases in the immunoglobulin IgA, IgG, and IgM content in RTI. The control group consisted of children suffering from RTI without any evident decreases in immunoglobulins. Serum bioelectrical properties were measured using impedance spectroscopy and immunoglobulins with an immunoturbidimetric analyzer. We found that the magnitude of serum impedance was significantly smaller in the sick children with immunoglobulin deficiency when compared with those of normal immunoglobulin profile, 134.1 ± 12.8 Ω vs 141.2 ± 16.9 Ω, respectively. We conclude that serum impedance, a parameter easily measured, has the potential to unravel the immunological underlining of RTI, particularly frequent and troubling infections in children. Screening for immunological disturbance is essential for the prompt implementation of a targeted treatment.

Background. The quality assessments of food products often involve evaluating their electrical properties, including impedance, permittivity and dielectric loss factor. The measurements of food electrical characteristics provide an interesting alternative to time-consuming and expensive methods based on chemical parameter measurements. This report describes investigations into the effects of frequency on the electrical properties of honey. Specifically, honey electrical properties were tested under an electromagnetic field with frequency ranging from 1 kHz to 1 MHz. Results and conclusion. Both experimental and numerical methods were used in this study. Double verification yielded identical results, which confirmed that the numerical method applied and the computational conditions were selected appropriately. The most important feature and the most significant advantage of the numerical approach is the possibility to predict the behavior of the actual object based on its mathematical model. It is much easier and faster to perform computer simulations than to perform the corresponding measurements under real-life conditions. Numerical simulations are also extremely useful when experiments are too dangerous to perform, i.e. when the electromagnetic field being studied can pose a threat to the health or life of a tested subject. The main drawbacks of computer simulations are the restraints of computing resources and the long duration of calculations.

In the experiment, the impact of magnetisation on cucumber seeds is examined with the use of Bitter magnets with a constant magnetic field. The magnetisation process is performed in three magnetic fields: low—200 mT, medium—1 T, and high—9 T for 15 and 60 min. After germination, the biometric parameters are determined. The results of this research show that cucumber after pre-treatment in a magnetic field of 1 T for 60 min has a similar germination capacity and root length as the control sample. However, cucumber seeds magnetised in a 1 T field for 60 min have a significantly higher dry weight than the control sample (5.50 ± 0.32 mg vs. 3.01 ± 0.18 mg). The magnetisation in 9 T for both 15 and 60 min shows that these samples have a significantly lower germination capacity (86.8 ± 4.4% and 81.4 ± 7.3% vs. 91.8 a ± 3.2%) and root length (1.78 ± 0.02 cm and 4.42 ± 0.83 cm vs. 8.21 ± 0.34 cm) compared to the control sample. The cucumber seeds pre-treated at 9 T have a significantly greater dry weight than the control sample. Additionally, our research shows that some magnetic field intensities and magnetisation durations inhibit root growth and limit germination. These results are also important as they indicate which values of magnetic fields should be avoided.