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...
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MDPI AG
2025-07-01
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| Series: | AgriEngineering |
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| author | Cannayen Igathinathane Rangaraju Visvanathan Ganesh Bora Shafiqur Rahman |
| author_facet | Cannayen Igathinathane Rangaraju Visvanathan Ganesh Bora Shafiqur Rahman |
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| description | 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. |
| format | Article |
| id | doaj-art-785e2cadb2fa4b2e9313acb887d7dca0 |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-07-01 |
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| series | AgriEngineering |
| spelling | doaj-art-785e2cadb2fa4b2e9313acb887d7dca02025-08-20T02:48:17ZengMDPI AGAgriEngineering2624-74022025-07-017720410.3390/agriengineering7070204Computer Vision-Based Multiple-Width Measurements for Agricultural ProduceCannayen Igathinathane0Rangaraju Visvanathan1Ganesh Bora2Shafiqur Rahman3Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USAA.D. Agricultural College and Research Institute, Tamil Nadu Agricultural University, Thiruchirappalli 620009, IndiaResearch and Technology Innovation, Fayetteville State University, Fayetteville, NC 28301, USAAgricultural Research and Development Program, Central State University, Wilberforce, OH 45384, USAThe 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.https://www.mdpi.com/2624-7402/7/7/204fruits and vegetablesgrading and sortinghorticultural cropsImageJimage processingphysical properties |
| spellingShingle | Cannayen Igathinathane Rangaraju Visvanathan Ganesh Bora Shafiqur Rahman Computer Vision-Based Multiple-Width Measurements for Agricultural Produce AgriEngineering fruits and vegetables grading and sorting horticultural crops ImageJ image processing physical properties |
| title | Computer Vision-Based Multiple-Width Measurements for Agricultural Produce |
| title_full | Computer Vision-Based Multiple-Width Measurements for Agricultural Produce |
| title_fullStr | Computer Vision-Based Multiple-Width Measurements for Agricultural Produce |
| title_full_unstemmed | Computer Vision-Based Multiple-Width Measurements for Agricultural Produce |
| title_short | Computer Vision-Based Multiple-Width Measurements for Agricultural Produce |
| title_sort | computer vision based multiple width measurements for agricultural produce |
| topic | fruits and vegetables grading and sorting horticultural crops ImageJ image processing physical properties |
| url | https://www.mdpi.com/2624-7402/7/7/204 |
| work_keys_str_mv | AT cannayenigathinathane computervisionbasedmultiplewidthmeasurementsforagriculturalproduce AT rangarajuvisvanathan computervisionbasedmultiplewidthmeasurementsforagriculturalproduce AT ganeshbora computervisionbasedmultiplewidthmeasurementsforagriculturalproduce AT shafiqurrahman computervisionbasedmultiplewidthmeasurementsforagriculturalproduce |