Affiliation:
1. Department of Machine, Technical Science Vocational School, Akdeniz University, Antalya 07070, Türkiye
2. Department of Management Information Systems, Faculty of Social Sciences and Humanities, Akdeniz University, Antalya 07600, Türkiye
3. Department of Mechatronics, Technical Science Vocational School, Akdeniz University, Antalya 07070, Türkiye
4. Department of Management and Entrepreneurship, Faculty of Entrepreneurship, National University of Science and Technology Polytechnic Bucharest, 060042 Bucharest, Romania
Abstract
For the design and sizing of equipment and structures in agricultural operations concerning the cherry tomato industry, especially harvesting operations and postharvest operations of the crops, it is very important to determine their mechanical properties. In the study, mass, length, thickness, width, geometric diameter, sphericity, surface area, rupture force, firmness, Poisson’s ratio, and modulus of elasticity were used as independent variables in the data set, and the dependent variable and deformation energy was estimated. Min–max normalization methods were used to increase the success and performance of the models. Three machine learning methods were utilized in the study, and statistical parameters, such as R2, MAE, and MSE, were used to evaluate the performance of the methods. The R2 of the artificial neural network (ANN), applied in the model as one of the machine learning methods, was found to be 96.8%, revealing the highest predictive power. Logistic regression with a 91.1% success rate, and decision tree regression with an 81.3% success rate, came second and third, respectively.
Funder
University of Science and Technology Polytechnic Bucharest through PubArt program
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
5 articles.
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