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Computer vision model for estimating the mass and volume of freshly harvested Thai apple ber (Ziziphus mauritiana L.) and its variation with storage days

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Title Computer vision model for estimating the mass and volume of freshly harvested Thai apple ber (Ziziphus mauritiana L.) and its variation with storage days
 
Creator Shekh Mukhtar Mansuri
Prem Veer Gautam
Dilip Jain
C. Nickhi
Pramendra
 
Subject Computer vision
Image processing
Machine learning
Regression
Support vector machine
 
Description Not Available
The physical properties of fruits are proportional to their mass and volume; this connection is used to determine the fruit qualities and in designing the novel postharvest machinery. The present study aimed to forecast the mass and volume of Thai apple ber (Ziziphus mauritiana L.) as a function of its physical properties measured using image processing techniques at different stages of ripening (1st day, 4th day, 7th day, and 10th day). The mass and volume models developed and analyzed the single variable regression, multilinear regressions, and mass regression based on volume. Among these models, linear support vector machine (SVM) was found appropriate. The experimental data analysis showed that the R2 of the linear SVM model for mass and volume of the projected area were 0.955 and 0.965, respectively. In contrast, for the multilinear regression model, R2 values were 0.967 and 0.972, respectively. For the mass prediction model, the R2 was 0.970 based on calculated volume showing a linear relationship. Thus, it was concluded that real-time measurement of physical properties of Thai apple ber using an image-processing technique to estimate the mass and volume is a precise and accurate approach.
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Date 2022-10-21T05:38:06Z
2022-10-21T05:38:06Z
2022-08-17
 
Type Research Paper
 
Identifier Mansuri, S. M., Gautam, P. V., Jain, D., & Nickhil, C. (2022). Computer vision model for estimating the mass and volume of freshly harvested Thai apple ber (Ziziphus mauritiana L.) and its variation with storage days. Scientia Horticulturae, 305, 111436.
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http://krishi.icar.gov.in/jspui/handle/123456789/74721
 
Language English
 
Relation Not Available;
 
Publisher ELSEVIER