Tillage is one of the most energy-intensive operations in crop production, leading to high fuel consumption and the emission of harmful gases such as CO₂ and NO_x. This study was conducted under real field conditions to explore how soil parameters influence variations in fuel use and exhaust emissions. A machine learning approach based on the XGBoost algorithm was applied to develop predictive models for CO₂ concentrations in exhaust gases and specific fuel consumption. The CO₂ prediction model achieved an accuracy exceeding 80%, while the model for fuel consumption reached over 65%. Although not optimized for high precision, these models offer a valuable basis for preliminary assessments and highlight the potential of data-driven approaches for improving energy efficiency and environmental sustainability in agricultural mechanization.

In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.

The research explores the drying kinetics of strawberry slices (5 mm thick with initial moisture content of 88.04% wb) through the application of both traditional mathematical models and advanced machine learning method. The study aims to optimize th...

Tillage in agricultural production is one of the most demanding in terms of the tractor's energy requirements, consuming a large amount of fuel and therefore releasing the harmful products of fuel combustion into the atmosphere, where carbon dioxide and nitrogen oxides are released. The experimental test was carried out under real operating conditions on the property, where an analysis was made of all the important parameters that can be correlated with ground resistance and thus the changes in the observed parameters, i.e. the changes in specific fuel consumption and the changes in exhaust emissions. Machine learning techniques were used as highly effective modelling techniques to predict the appropriate tractor operating conditions and soil condition. XGBoost model was selected due to its flexibility and high level of learning. The models were built to predict the CO2 content in the exhaust gases and the specific fuel consumption. Although the statistical parameters were not the highest, the prediction of CO2 content based on the data analysis has an accuracy of over 80%, and the prediction of specific fuel consumption is over 65%, which can be used for a quick or initial analysis, but also represents a challenge for further research on this topic.

In this work, variable temperature drying regimes, mass of the samples, and the blanching pretreatments were varied to determine the influence of these parameters on the kinetics and specific energy consumption of red beetroot chips during convective drying. Beetroot samples used for the analysis were fresh and blanched slices of 5 mm thickness. Three different temperature regimes were used during drying experiments: first regime (I) was drying at a constant temperature of 70?C, second regime (II) was performed with two-stage drying at temperatures of 80?C and 55?C, and third regime (III) was conducted with three-stage drying at temperatures of 80?C, 60?C, and 55?C, respectively. Results obtained from the experiments were analyzed from the aspects of equilibrium moisture content, drying time, specific energy consumption and drying kinetics including determination of effective moisture coefficient. It was concluded that stage drying strongly affects equilibrium moisture content, while blanching pretreatment increases initial moisture content. The drying time was strongly dependent on the initial mass of the samples, while the energy consumption was depended, besides on the mass, also on drying regime, and it was found to be smallest for two-stage drying. Visually, beetroot had the typical red color for all samples at the beginning, with notable shrinkage effect at the end of the drying process.

Machine learning (ML) is a key technology driving the modernisation of agriculture. It enables large data sets to be analysed and precise decisions to be made at all stages of agricultural production. ML is used for soil analysis, plant disease detection, weed control, crop species identification and harvest optimisation. Various techniques such as supervised, unsupervised and reinforcement learning help to improve the accuracy of predictions and decisions. Artificial neural networks (ANN), in particular deep neural networks (DNN) and convolutional neural networks (CNN), efficiently analyse images and numerical data and enable precise management of agricultural practises. These technologies contribute to sustainability by reducing the negative impact on the environment and optimising the use of resources. While significant progress has already been made, there is still potential for further development of ML models that cover all phases of the agricultural cycle and make precision agriculture more efficient and safer.