Abundant biomass is a potential energy source. However, it possesses several challenges when considered for energy applications. Torrefaction, a thermal pretreatment process can improve the properties of biomass as energy source. This study focused on comparing effect of torrefaction operating parameters on agricultural and wood wastes properties as fuel. The physiochemical properties, composition, moisture-biomass interaction and ash melting behavior were determined. The result show that higher torrefaction temperature and longer residence time increased lignin content, reduced hemicellulose and cellulose content. The moisture uptake of torrefied biomass was reduced in the range 2.47–9.94% compared with raw biomass depending on torrefaction temperature that indicate torrefied biomass was more hydrophobic than raw biomass. The moisture adsorption isotherm curve shows type II isotherm based on the Brunauer-Emmett-Teller’s (BET) classification and was best described by the Oswin model. In addition, torrefaction treatment showed significant influence on the melting behavior of the biomass ash. Especially for agricultural wastes, the fouling tendency shifted from serious range to low range with torrefaction treatment. Torrefaction showed promise for improving fuel characteristics of the studied biomass.

Additive manufacturing (AM) is considered the enabling technology for topology optimized components, with its unparalleled, almost free-form design freedom. Over the past decade, AM of electromagnetic materials has evolved into a promising new area of research. Considerable efforts have also been invested by the electrical machine (EM) research community to develop and integrate novel additive components. Several challenges remain, however, in printing soft magnetic flux guides—most prominently, reducing the induced eddy currents to achieve competitive AM core efficiency. This paper demonstrates the workflow of laser additive manufacturing magnetic cores with superior magnetic properties to soft magnetic composites (at 50 Hz excitation): describing the workflow, parameter tuning for both printing and annealing, and shape optimization. Process optimization yielded the optimal energy density of 77 J/mm³ and annealing temperature of 1200 °C, applied to prepare the samples with the highest relative density (99.86%), lowest surface roughness R_z (0.041 mm), minimal hysteresis losses (0.8 W/kg at 1.0 T, 50 Hz), and ultimate yield strength of 420 MPa. For Eddy current suppression, the sample (5 × 5 × 60 mm toroid) with bi-directional grading reached specific core losses as low as 1.8 W/kg (W_10,50). Based on the findings, the advantages and disadvantages of AM graded cores are discussed in detail.

Machine learning algorithms are increasingly used to enhance agricultural productivity cost-effectively. A critical task in precision agriculture is locating a plant’s root collar. This is required for the site-specific fertilization of the plants. Though state-of-the-art machine learning models achieve stellar performance in object detection, they are often sensitive to noisy inputs and variation in environment settings. In this paper, we propose an innovative technique of smooth perturbations to improve the robustness of root collar detection tasks using the YOLOv5 neural network model. We train a YOLOv5 model on blueberry image data for root collar detection. A small amount noise is added as a smooth perturbation to the bounding box of dimensions 50× 50, and this perturbed image is fed for training. Furthermore, we introduce an additional test set that represents the out-of-distribution (O.O.D.) case by applying Gaussian blur on test images to simulate particle situation. We use three different image datasets to train our model, the (i) Estonian blueberry, (ii) Serbian blueberry image, and (iii) public dataset sourced from Roboflow datasets, of sample size 118, 2779, and 2993, respectively. We achieve an overall precision of 0.886 on perturbed blueberry images compared to 0.871 on original (unperturbed) images for the O.O.D. test set. Similarly, our smooth perturbation training has achieved an mAP of 0.828, which significantly increases against the result of normal training, which only reaches 0.794. The result proves that our proposed smooth perturbation is an effective method to increase the robustness and generalizability of the object detection task.

For precise fertilization of blueberry plants, it is technologically the easiest and most suitable option to use a volumetric filling, for which it can be presumed that it is possible to precisely dose the fertilizer for each plant by grams. For setting up a volumetric filler, it is necessary to know parameters such as the size of the fertilizer particles and their bulk density. The aim of this research is to determine the granulometric parameters and their effect, which is done by measuring up three different fertilizers (SQM Qrop K, Memon Siforga, Substral): width, height, and length of 100 randomly selected fertilizer particles as well as the volumes and weights of 100 particles in 10 repetitions. According to the measurements, the average diameters of fertilizer particles were found as well as the average mass, volumes, and bulk density. A Mahr Digital Caliper 16EWRi 0–150 mm was used to measure the diameters of the fertilizer granules. A Yxlon FF35 computer tomograph was used to accurately scan particles. The analytical scale, Kern ABJ 220-4NM, was used to determine mass. The volumes were measured, using measuring glasses, with one having a maximum volume of 10 mL in 0.2 mL increments and another having a maximum volume of 100 mL in 1 mL increments. Descriptive statistics analysis was performed in Microsoft Excel. It turned out that the average diameters (3.68 vs. 3.64 vs. 4.29 mm) and bulk densities (0.928 vs. 0.631 vs. 0.824 g cm⁻³) of the three fertilizers differed far from each other, meaning that the given volume could be filled with different amounts of fertilizer. Equations between mass and weight were formed according to the measurements. As a result, it was found that a volumetric filler can be used for fertilizing blueberry plants precisely, but it demands adjusting the filler each time in the situation, which is defined by the variety of blueberry plants: their age, size, and health.

Soil disturbance will remarkably alter physical properties of the soil  and the recovery of the structure as well as mechanical strength recovery will take  years. Typically deforestation works, e.g. needed for the establishment of open military training areas, seriously influences soil mechanical state. Deforestation works involve processes like felling of trees, uprooting of stumps followed by levelling of ground. For the establishment of more favourable conditions for grass cover development, tree stumps and felling residues are mixed into the soil with a rotary thriller. Therefore, the final disturbed ground  has low density and high porosity resulting in low mechanical strength. On the  contrary, military training includes high intensity movement by soldiers and trafficking by vehicles. Thus, these types of activities presume stable soil conditions with a high mechanical strength. The aim of our research was to investigate soil density status of disturbed grassland with computed tomography. The soil samples were collected from the undisturbed area and from the grassland which was treated with the rotary thriller 2 years ago. The undisturbed soil samples were collected using plastic cylinders (of 10 cm diameter and 20 cm height) in 3 repetitions.  For comparison, smaller soil samples (5.3 cm diameter and 4 cm height) were obtained at 0, 5, 10, 15, 20, 30 cm depths  for determining the soil bulk density. In the  laboratory, the high resolution industrial computed tomography system Yxlon FF 35 CT was used with the larger soil samples. The soil samples were analyzed using the software Volume Graphics VGSTUDIO MAX 3.2.  Our results indicated the possibility of determination of distribution of pores in the soil and the changes in the porosity of soils depending on the soil treatment and the type.