The implementation of agronomic activities, based on the use of biostimulants, is an important element of agroecological practices. Therefore, comprehensive research was carried on the use of biostimulants. A field experiment was performed in 2016–2018 with common bean of Mexican Black cultivar. In particular growing seasons, bean plants were treated with Kelpak SL (seaweed extracts) and Terra Sorb Complex (free amino acids) in the form of single and double spraying with two solutions concentrations. According to the obtained data, application of biostimulants increased the yield of bean. Better results were observed after the use of Kelpak SL. The application of preparations influenced nutritional and nutraceutical quality of bean seeds. Terra Sorb Complex caused the highest increase in proteins level. In the light of achieved data, biostimulants in similar level decreased the starch accumulation. The most promising results, in the context of nutraceutical value of bean, were obtained in the case of increasing level of fiber. A positive impact of biostimulants on the seeds antioxidant potential was noted, expressed by the increased synthesis of phenolics, flavonoid, anthocyanins and antioxidant activities. Results of this study, directly indicate economic benefits from the use of biostimulants, which are extremely important to the farmers.

Foodwaste (hereinafter, FW) is the most voluminous solid waste and its amount is growing rapidly all over the world. The turning of FW into biogas via anaerobic fermentation is widely recognized as an environmentally responsible and economically reasonable option. Based on the knowledge obtained from agricultural biogas stations, the current methods of FW fermentation management are based on balancing the ratio of total carbon and nitrogen. However, it was repeatedly and independently reported that the stability of this process is low, resulting in many concessions in terms of prolonged hydraulic retention time or reduced biogas yield. Hence, biochemical as well as economic performance of the process is balanced by mixing of FW with agricultural residues. FW samples of various origin were collected and biochemically analyzed. The data indicate that FW originating from homes and luxury restaurants tends to be lignocellulose-based, whereas the levels of crude fiber (25% up to 27%) are higher than those from agricultural feedstock (18%). In contrast, FW from school canteens and inexpensive restaurants tends to be starch-based with high levels of amyloids (21% up to 23%) and fat (5% up to 7%). A novel method better reflecting the bioavailability of carbon and nitrogen to anaerobic consortia is proposed. It is demonstrated that the previous optimization methods could somehow reflect the availability of nutrients in agricultural feedstock, as carbonaceous and nitrogen sources are relatively equally biodegradable. Nevertheless, the biodegradability of FW is considerably different, which is why higher amounts of proteins and lipids lead to increased levels of ammonia and sulfide, resulting in an inhibitory effect on the metabolism of anaerobic consortia. Optimizing the anaerobic fermentation of FW by the new method outperforms the previous technique and makes it possible to process FW more intensively, or, more precisely, with higher profitability and lower proportion of ballast agricultural feedstock.

Rolling bearings play a pivotal role in rotating machinery. The remaining useful life prediction and fault diagnosis of bearings are crucial to condition-based maintenance. However, traditional data-driven methods usually require manual extraction of features, which needs rich signal processing theory as support and is difficult to control the efficiency. In this study, a bearing remaining life prediction and fault diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN) has been proposed. First, the STFT was adopted to construct time-frequency maps of the unprocessed original vibration signals that can ensure the true and effective recovery of the fault characteristics in vibration signals. Then, the training time-frequency maps were used as an input of the CNN to train the network model. Finally, the time-frequency maps of testing signals were inputted into the network model to complete the life prediction or fault identification of rolling bearings. The rolling bearing life-cycle datasets from the Intelligent Management System were applied to verify the proposed life prediction method, showing that its accuracy reaches 99.45%, and the prediction effect is good. Multiple sets of validation experiments were conducted to verify the proposed fault diagnosis method with the open datasets from Case Western Reserve University. Results show that the proposed method can effectively identify the fault classification and the accuracy can reach 95.83%. The comparison with the fault diagnosis classification effects of backpropagation (BP) neural network, particle swarm optimization-BP, and genetic algorithm-BP further proves its superiority. The proposed method in this paper is proved to have strong ability of adaptive feature extraction, life prediction, and fault identification.

The share of renewables is rising rapidly, especially in developed countries. Biogas production from purpose-grown phytomass is undergoing the fastest growth. The trend is linked to the production of vast amounts of fermentation residues. However, it has been repeatedly and independently reported that intensive or long-term application of fermentation residues into arable land changes soil structure, resulting in sharp degradation of its fertility. This is now compensated by more intense use of agrochemicals and additional agrotechnical operations such as biochar addition. However, the increased cost is beginning to threaten the economic sustainability of biogas production. Given the fact that the production of biogas from purpose-grown phytomass has become a strong pillar of the electricity grid, the threat to soil fertility may endanger its stability. The quality and quantity of soil organic matter (SOM) and in particular its stable organic fractions with ion-exchange properties (SOF) that determine soil fertility, or, more precisely, the transport of nutrients and their availability for plant growth, were investigated in detail. A novel, undemanding and quick method allowing the analysis and interpretation of SOM and its SOF was proposed and compared with conventional methods. It was confirmed that the adaptation of the new method enables farmers to better choose organic and mineral fertilizers and corresponding agronomic operations, so the soil can provide higher yields and an increased water retention capacity (up to 7%), which results in improved water retention during extreme rainfalls or droughts, altogether lowering the cost of purpose-grown phytomass, or, more precisely, improving the economy of biogas production. Abbreviations: SOM: Soil organic matter; SOF: Stable organic fractions of SOM with ion-exchange properties; WRC: Water retention capacity; ARC: Air retention capacity; TOC: Total organic carbon; CEC: Cation exchange capacity; HA: Humic acids; FA: Fulvic acids; NHSOM: Non-humified soil organic matter; PSOM: Primary soil organic matter; C_ox: Oxidizable carbon

This paper considers the evolution of processes applied in agriculture for field operations developed from non-organized handmade activities into very specialized and organized production processes. A set of new approaches based on the application of metaheuristic optimization methods and smart automatization known as Agriculture 4.0 has enabled a rapid increase in in-field operations’ productivity and offered unprecedented economic benefits. The aim of this paper is to review modern approaches to agriculture machinery movement optimization with applications in sugarcane production. Approaches based on algorithms for the division of spatial configuration, route planning or path planning, as well as approaches using cost parameters, e.g., energy, fuel and time consumption, are presented. The combination of algorithmic and economic methodologies including evaluation of the savings and investments and their cost/benefit relation is discussed.