The article deals with the most effective primary utilization and subsequent processing of the main waste of machine-building enterprises-metal chips. To solve the above problems, the recycling of metal waste the most effective method is a comprehensive approach to the conditions of machine-building enterprises working in the conditions of the automated production consists of solutions to several problems, the main of which are submitted to the necessary properties of the chips produced and optimization of the technological equipment used for initial processing and subsequent briquetting of the chip. The solution of the problem of obtaining chips of the specified characteristics after the analysis of existing methods of its solution is proposed to be performed by applying the method of preliminary local physical impact on the surface of the workpiece. The proposed method makes it possible to obtain chips of such parameters that will meet the technical conditions of the type of automatic line for its processing and disposal, which is available in a particular production. The problem of optimization of the technological line of primary processing of chips is proposed to be solved on the basis of preliminary engineering analysis of chip crushing devices, which allows you to choose the optimal geometry of the cutter inserts depending on the type and material of the chips being crushed. The proposed set of measures should help machine-building enterprises to increase the profitability of production.

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.

Assessment of the harvesting costs of different combine harvester fleets | Jüri Olt, Keio Küüt, Risto Ilves, Arne Küüt | Agricultural Journals

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.