Weight determination of mango (Mangifera indica l.) by frequency analysis
Abstract
© 2019, Int. Comm. of Agricultural and Biosystems Engineering. All rights reserved. The study involved the fabrication and formulation of an alternative system that use natural frequency analysis for weight measurement of mango fruits. The system was mainly composed of an ADXL345 accelerometer, Arduino UNO board, stainless steel cantilever beam, frames and breadboards, and two developed program algorithms. The Frequency Detector (FD) and Frequency and Weight Detector (FWD) algorithms were written using National Instruments LabVIEW 2014. Thirty (30) carabao mango fruits of varying sizes were used during the testing process. The actual weights of the samples were obtained using a digital weighing scale. Size classification of each mango fruits based on actual weight and based on the weights predicted by the system involving natural frequency analysis was performed. The percent error using natural frequency analysis was 2.89%. Moreover, the accuracy of natural frequency analysis in size classification was determined and found that 29 out of the 30 samples were correctly classified. A verification run was performed to check the similarity of the predicted and detected frequencies of the new set of fruit samples. A t-test analysis showed that the predicted and detected frequencies had no significant difference. The average percent error between the predicted and detected frequencies was computed to be 1.10%. The verification run established that the frequency analysis could be used to estimate the weight of mango fruits and classify them in size.
Source or Periodical Title
Agricultural Engineering International: CIGR Journal
Page
186-194
Document Type
Article
Subject
Frequency analysis, Natural frequency, Program algorithm, T-test, Weight
Recommended Citation
Menguito, Jerson Jose Talag; Suministrado, Delfin C.; Zubia, Omar F.; Onal, Mark Keylord S.; and Quilloy, Erwin P., "Weight determination of mango (Mangifera indica l.) by frequency analysis" (2021). Journal Article. 949.
https://www.ukdr.uplb.edu.ph/journal-articles/949