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Open AccessArticle
Critical Drop Height Prediction of Loquat Fruit Based on Some Engineering Properties with Machine Learning Approach
by
Onder Kabas
Onder Kabas 1,2,
Uğur Ercan
Uğur Ercan 3,* and
Georgiana Moiceanu
Georgiana Moiceanu 4,*
1
Department of Machine, Technical Science Vocational School, Akdeniz University, 07070 Antalya, Türkiye
2
Department of Rural and Agri-Food Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
3
Department of Informatics, Akdeniz University, 07070 Antalya, Türkiye
4
Department of Entrepreneurship and Management, Faculty of Entrepreneurship, Business Engineering and Management, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(7), 1523; https://doi.org/10.3390/agronomy14071523 (registering DOI)
Submission received: 10 June 2024
/
Revised: 2 July 2024
/
Accepted: 11 July 2024
/
Published: 13 July 2024
Abstract
The lowest height at which a product can fall without suffering severe harm is known as the “critical drop height” for agricultural products. It is a crucial factor to take into account for crops like loquats that are prone to bruising or damage upon impact. By establishing the minimum altitude at which the product can be dropped without experiencing substantial harm, suitable processing procedures may be established from harvest to the end consumer, thereby preserving product quality and worth. The critical drop height can be ascertained through swift, affordable, non-destructive, and non-traditional methods, rather than time-consuming and expensive laboratory trials. In the study, we aimed to estimate the critical drop height for loquat fruit using machine learning methods. Three different machine learning methods with different operating principles were applied. R2, MAE, RMSE, and MAPE metrics were used to assess the models. There were no obvious differences in both the comparisons within the models, namely the training and test results and the mutual comparisons of the models. However, with a slight difference, the SVMs model performed better in the training data set, and the ETs model performed better in the test data set. Plots were drawn to visualize model performances, and the results obtained from the plots and metrics support each other.
Share and Cite
MDPI and ACS Style
Kabas, O.; Ercan, U.; Moiceanu, G.
Critical Drop Height Prediction of Loquat Fruit Based on Some Engineering Properties with Machine Learning Approach. Agronomy 2024, 14, 1523.
https://doi.org/10.3390/agronomy14071523
AMA Style
Kabas O, Ercan U, Moiceanu G.
Critical Drop Height Prediction of Loquat Fruit Based on Some Engineering Properties with Machine Learning Approach. Agronomy. 2024; 14(7):1523.
https://doi.org/10.3390/agronomy14071523
Chicago/Turabian Style
Kabas, Onder, Uğur Ercan, and Georgiana Moiceanu.
2024. "Critical Drop Height Prediction of Loquat Fruit Based on Some Engineering Properties with Machine Learning Approach" Agronomy 14, no. 7: 1523.
https://doi.org/10.3390/agronomy14071523
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