Computer Vision-Based Multiple-Width Measurements for Agricultural Produce
The most common size measurements for agricultural produce, including fruits and vegetables, are length and width. While the length of any agricultural produce can be unique, the width varies continuously along its length. Single-width measurements alone are insufficient for accurately characterizin...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/7/7/204 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The most common size measurements for agricultural produce, including fruits and vegetables, are length and width. While the length of any agricultural produce can be unique, the width varies continuously along its length. Single-width measurements alone are insufficient for accurately characterizing varying width profiles, resulting in an inaccurate representation of the shape or mean dimension. Consequently, the manual measurement of multiple mean dimensions is laborious or impractical, and no information in this domain is available. Therefore, an efficient alternative computer vision measurement tool was developed utilizing ImageJ (Ver. 1.54p). Twenty sample sets, comprising fruits and vegetables, with each representing different shapes, were selected and measured for length and multiple widths. A statistically significant minimum number of multiple widths was determined for practical measurements based on an object’s shape. The “aspect ratio” (width/length) was identified to serve as an effective indicator of the minimum multiple width measurements. In general, 50 multiple width measurements are recommended; however, even 15 measurements would be satisfactory (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.0%</mn><mo>±</mo><mn>0.6%</mn></mrow></semantics></math></inline-formula> deviation from 50 widths). The developed plugin was fast (734 ms ± 365 ms CPU time/image), accurate (>99.6%), and cost-effective, and it incorporated several user-friendly and helpful features. This study’s outcomes have practical applications in the characterization, quality control, grading and sorting, and pricing determination of agricultural produce. |
|---|---|
| ISSN: | 2624-7402 |