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Assessment of Load on Threshing Bar During Soybean Pod Threshing
이건호,문병은,Basak Jayanta Kumar,김나은,Paudel Bhola,전성우,국정후,강명용,고한종,김현태 한국농업기계학회 2023 바이오시스템공학 Vol.48 No.4
Purpose This study investigated the threshing load experienced by the threshing bar when it collides with soybean pods during the threshing process. Methods The threshing machine was designed and modeled in the commercial design software, Solidworks, referring to the threshing compartment of a commercial thresher available in Korea. The threshing load was simulated using a commercial simulation software and was recorded for each rotating speed. The actual load experienced by the threshing bar during soybean pod threshing was measured by a strain gauge attached to the threshing bar. Load data from the strain gauge was collected at each microsecond interval. Results The results of the field test and simulations showed that the load gap range varied from about 0.15 N at 250 rpm to about 1.00 N at 400 rpm rotational speed. It was observed that stress increases with an increase in rotational speed, which was similar in both simulation and field experiment. The probable reasons for this difference were the lack of consideration for the joint characteristics between the threshing bar and drum, the properties of the soybean pods, and the influence of gravity and pressure on the field test results.
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Classification of strawberry ripeness stages using machine learning algorithms and colour spaces
Karki Sijan,Basak Jayanta Kumar,Paudel Bhola,Deb Nibas Chandra,김나은,국정후,강명용,김현태 한국원예학회 2024 Horticulture, Environment, and Biotechnology Vol.65 No.2
Accurate classifi cation of strawberry ripeness is a crucial aspect of ensuring high-quality food products, optimizing harvest ing and storage processes, and promoting consumer health. Although several non-destructive computer vision-based systems have been developed for this purpose, the infl uence of diff erent colour spaces on machine-learning model performance dur ing the ripeness stage classifi cation of strawberries remains underexplored. In this context, three machine-learning models, namely Gaussian Naïve Bayes (GNB), support vector machine (SVM) and feed-forward artifi cial neural networks (FANN), were combined with four colour spaces (RGB, HLS, CIELab and YCbCr) and biometrical characteristics to evaluate the eff ectiveness of colour spaces on the performance of machine-learning models for classifying strawberry ripeness. For this purpose, 1210 samples were collected and manually classifi ed into four ripeness stages. A dataset was created by combining each colour space value, biometrical properties, and corresponding ripeness stage, which was used as inputs to the models. The results indicated that FANN with CIELab colour space achieved the highest accuracy of 96.7%, followed by GNB and SVM, both having equal accuracy of 95.46% in CIELab colour space. The least accuracy of 92.15% was observed in RGB colour space with the GNB classifi er. In this study, the unripe and over-ripe stages were more accurately classifi ed, while intermediate ripening stages proved to be more challenging for the models. Furthermore, the accuracy of models was observed to be infl uenced by both the colour space and classifi cation model selected. Additionally, further research is needed to investigate other features that could improve the performance of models for strawberry ripeness classifi cation.
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