Impact characteristics of sugar beet root during postharvest storage

Kołodziej, Paweł; Gołacki, Krzysztof; Boryga, Marek

doi:10.31545/intagr/110810

Cited by 6 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pendulum was connected with the angular displacement sensor RON 275 (Heidenhain, Traunreut, Germany) with the accuracy of 0.005°. The sensor recording inclination angle change was connected to the measuring card (SBC-68, National Instruments, Warsaw, Poland), transmitting signals to the application working in the LabView environment [ 9 , 10 , 11 , 30 ]. The right humerus was fixed with quick-drying epoxy glue in a PVC tube with an internal diameter of 15 mm and a 20 mm height.…”

Section: Methodsmentioning

confidence: 99%

“…The energy was found from the area under the loading curve ( Figure 3 ) by integrating to the maximum force (Graph, free software, version 4.4.2). It could also be determined from Equation (1), assuming that the kinetic energy of the hammer is many times larger at the time of impact compared to the energy required for the sample fracture [ 9 ]:

where v —the velocity of the hammer at the impact time, assumed to be constant (m·s − 1 ); F(t) —the force registered during the test as a function of time (N); t —the time from the beginning of the test to the point where the maximal impact force value was reached.…”

Section: Methodsmentioning

confidence: 99%

“…Zhai et al [ 4 ] analyzed the loading rate effect on the fracture toughness of porcine bone specimens and found that the fracture initiation of cortical bone decreased as the loading rate increased. The impact of free-falling mass was well described for plant tissues [ 9 , 10 , 11 ] or engineering materials [ 12 , 13 ]. With regard to bone, there are still few studies describing the mechanical parameters under the impact loading condition.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of Nesfatin-1 on Rat Humerus Mechanical Properties under Quasi-Static and Impact Loading Conditions

et al. 2022

Self Cite

View full text Add to dashboard Cite

The investigations on the response of bone tissue under different loading conditions are important from clinical and engineering points of view. In this paper, the influence of nesfatin-1 administration on rat humerus mechanical properties was analyzed. The classical three-point bending and impact tests were carried out for three rat bone groups: control (SHO), the humerus of animals under the conditions of established osteopenia (OVX), and bones of rats receiving nesfatin-1 after ovariectomy (NES). The experiments proved that the bone strength parameters measured under various mechanical loading conditions increased after the nesfatin-1 administration. The OVX bones were most susceptible to deformation and had the smallest fracture toughness. The SEM images of humerus fracture surface in this group showed that ovariectomized rats had a much looser bone structure compared to the SHO and NES females. Loosening of the bone structure was also confirmed by the densitometric and qualitative EDS analysis, showing a decrease in the OVX bones’ mineral content. The samples of the NES group were characterized by the largest values of maximum force obtained under both quasi-static and impact conditions. The energies absorbed during the impact and the critical energy for fracture (from the three-point bending test) were similar for the SHO and NES groups. Statistically significant differences were observed between the mean Fi max values of all analyzed sample groups. The obtained results suggest that the impact test was more sensitive than the classical quasi-static three-point bending one. Hence, Fi max could be used as a parameter to predict bone fracture toughness.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of Nesfatin-1 on Rat Humerus Mechanical Properties under Quasi-Static and Impact Loading Conditions

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The main factors affecting the magnitude and characteristics of damage to root crops are soil moisture, varietal characteristics, and size of plants, as well as the design and operation of beet harvesters [3]. Sugar beet can get mechanical damage in all processes and components of the harvester [4]. Modern beet harvesters are high-tech complexes that include a unit for removing and grubbing, a separating complex for cleaning root crops, and a bunker for beets with an unloading conveyor.…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

et al. 2022

View full text Add to dashboard Cite

Detecting sugar beetroot crops with mechanical damage using machine learning methods is necessary for fine-tuning beet harvester units. The Agrifac HEXX TRAXX harvester with an installed computer vision system was investigated. A video camera (24 fps) was installed above the turbine, which receives the dug-out beets after the digger and is connected to a single-board computer. At the preprocessing stage, static and insignificant image details were revealed. Canny edge detector and excess green minus excess red (ExGR) method were used. The identified areas were excluded from the image. The remaining areas were glued with similar areas of another image. As a result, the number of images entering the second stage of preprocessing was reduced by half. Then Otsu's binarization was used. The main stage of image processing is divided into two sub-stages: detection and classification. The improved YOLOv4tiny method was chosen for root crop detection using a single-board computer (SBC). This method allows processing up to 14 images of 416 × 416 pixels with 86% precision and 91% recall. To classify root crop damage, we considered two algorithms as candidates: 1. bag of visual words (BoVW) with a support vector machine (SVM) classifier using histogram of oriented gradients (HOG) and scale-invariant feature transform (SIFT) descriptors; 2. convolutional neural networks (CNN). Under normal lighting conditions, CNN showed the best accuracy, which ranged from 97% to 100%, depending on the damage class. The implemented methods were used to detect and classify blurred images of sugar beetroots, which were previously rejected. For improved YOLOv4-tiny precision was 74% and recall was 70%. CNN classification accuracy ranged from 90% to 95% depending on the root crops damage class.

show abstract

“…Sugar beet harvesters have improved significantly in recent years; however, the mechanical damage still occurs during harvesting in the harvester machines. According to [4], the mechanical damage of sugar beets can occur in all processes and components of the harvester and can be categorized into: (i) damage and breaking of the beet due to harvesting and improper trimming in the topper unit, (ii) damage in cleaning and transporting sections, (iii) those from roots falling during transport and unloading stages in the tank or on the field. Damage may be Agriculture 2021, 11, 1111 2 of 13 increased by mechanical stress during cleaning [5] and transport to the tank of the harvester.…”

Section: Introductionmentioning

confidence: 99%

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

2021

View full text Add to dashboard Cite

Mechanical damages of sugar beet during harvesting affects the quality of the final products and sugar yield. The mechanical damage of sugar beet is assessed randomly by operators of harvesters and can depend on the subjective opinion and experience of the operator due to the complexity of the harvester machines. Thus, the main aim of this study was to determine whether a digital two-dimensional imaging system coupled with convolutional neural network (CNN) techniques could be utilized to detect visible mechanical damage in sugar beet during harvesting in a harvester machine. In this research, various detector models based on the CNN, including You Only Look Once (YOLO) v4, region-based fully convolutional network (R-FCN) and faster regions with convolutional neural network features (Faster R-CNN) were developed. Sugar beet image data during harvesting from a harvester in different farming conditions were used for training and validation of the proposed models. The experimental results showed that the YOLO v4 CSPDarknet53 method was able to detect damage in sugar beet with better performance (recall, precision and F1-score of about 92, 94 and 93%, respectively) and higher speed (around 29 frames per second) compared to the other developed CNNs. By means of a CNN-based vision system, it was possible to automatically detect sugar beet damage within the sugar beet harvester machine.

show abstract

Impact characteristics of sugar beet root during postharvest storage

Cited by 6 publications

References 25 publications

Effect of Nesfatin-1 on Rat Humerus Mechanical Properties under Quasi-Static and Impact Loading Conditions

Effect of Nesfatin-1 on Rat Humerus Mechanical Properties under Quasi-Static and Impact Loading Conditions

Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

Contact Info

Product

Resources

About