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Review of Seed Hemp (Cannabis sativa L.) Harvesting Techniques and the Challenges of Harvesting Technologies for This Crop
Industrial hemp (Cannabis sativa L.) harvesting for grain represents a critical technological bottleneck in the modern supply chain, driven by a fundamental conflict between the plant’s resilient morphology and standard agricultural machinery. This review provides an analytical synthesis of harvesting methodologies, evaluating their performance against specific biological constraints such as extreme plant height (up to 4.5 m), high tensile fiber strength, and indeterminate ripening. Data synthesis reveals that hemp cutting is approximately 80 times more energy-intensive than for traditional forage crops, requiring an average maximum force of 243 N per stem. Comparative analysis demonstrates that while conventional whole-plant harvesting faces seed losses ranging from 26% to 46%, selective systems like specialized panicle mowers reduce these losses to nearly 2 kg·ha−1 by targeting only the mature inflorescences. To ensure seed integrity and operational stability, the review identifies concrete technological priorities: the use of abrasion-resistant alloys for cutting edges, the implementation of non-binding shaft shielding (e.g., ABS piping), and a 40–50% reduction in threshing cylinder speeds compared to cereal settings. Future advancements must focus on specialized, high-clearance selective machinery and adaptive control systems to reconcile hemp’s unique physiology with industrial-scale efficiency.
Non-Invasive Electrical Detection of Screw Wear in an Industrial Extrusion System
Reliable detection of mechanical wear is essential for maintaining operational stability and reducing unplanned downtime in industrial extrusion systems. This study investigates non-invasive detection of screw wear using operational electrical measurements acquired from a single-screw industrial extruder. Electrical parameters were recorded under steady-state processing conditions for healthy and worn screw configurations to determine whether measurable differences in electromechanical behavior could support condition assessment. The collected signals were segmented into 1429 labeled samples and evaluated using statistical and time–frequency analyses. Mean electrical parameters were compared between technical states, and independent samples Welch t-tests confirmed statistically significant differences in phase voltage for all monitored phases (p < 0.001). Continuous wavelet transform was applied to capture non-stationary signal characteristics, enabling extraction of energy- and entropy-based descriptors associated with variations in mechanical load. The derived features were subsequently used for automated classification of machine condition. The results revealed consistent increases in phase voltage for the worn screw ranging from 0.50% to 0.61%, indicating a stable shift in the electrical operating characteristics of the drive system. Supervised classification achieved an accuracy of 96.2% (289 of 300 samples correctly classified in the testing subset), demonstrating reliable separability between technical states without the need for additional vibration instrumentation. These findings confirm that operational electrical signals provide diagnostically relevant information for screw wear detection and support scalable implementation of electrical condition monitoring in industrial extrusion systems.
Aktualny stan prac wdrożeniowych z zakresu robotyzacji prac leśnych prowadzonych w Katedrze Użytkowania Lasu i Techniki Leśnej UR w Krakowie
Hazards caused by stones in the arable layer of agricultural fields and methods for their removal
The current importance of using various methods of mechanical weed control. These methods are based on the action on the growing undesirable plants (weeds) in the soil of the working elements of tools carrying out mechanical weed elimination (knives, wide blades, chisels) used, for example, in weeders. Their direct contact with stones deposited in the cultivation layer of the field results in excessive, faster frictional wear or even destruction. Other machines at risk of damage from contact with stones are, for example, the cutting units of crop harvesting machines and the working units of combine harvesters for harvesting winter squash and sugar beet. The article identifies the hazards caused by stones in agricultural fields, related to hampering field work and worsening plant vegetation and harvesting conditions. Various stone removal methods are described and illustrated with technical examples. Spot removal of large stones and two-stage and one-stage harvesting from the entire field area are discussed. It was pointed out that the diverse range of machines available on the market makes it possible to fully mechanise the removal of stones, and that the high costs of such a procedure can be fully compensated by the removal of hazards to the working units of the machines and an increase in the quality of the crop.
Carbon footprint generated during plant production – literature review and own research
The publication is addressed to individuals and entities that deal with trade and implementation of new agricultural machinery. The paper presents identified sources causing greenhouse gas emissions into the atmosphere in the plant production process. The work was divided into several parts: the topic of gases causing the greenhouse effect was generally presented, then the gases that were most responsible for the said effect were identified. A method for determining the carbon footprint of agrotechnical treatments was also proposed, based on the results of research carried out using a machine aggregated with a tractor through a measurement frame specially designed and built for this purpose. The aforementioned work was carried out to identify and illustrate many agrotechnical treatments against a common background. The preliminary studies conducted in field conditions allow us to assess the sense of conducting this type of research based on simple to measure parameters. The aspect that remains to be solved is the development of an algorithm that, after taking into account parameters such as the value of working resistance or the type of machine, will determine the carbon footprint.